are small zooplankton found in freshwater inland lakes and are thought to switch their mode of reproduction from asexual to sexual in response to extreme temperatures (Mitchell 1999). Lakes containing have an average summer surface temperature of 20°C (Harper 1995) but may increase by more than 15% when expose to warm water effluent from power plants, paper mills, and chemical industry (Baker et al. 2000). Could an increase in lake temperature caused by industrial thermal pollution affect the survivorship and reproduction of ?
The sex of is mediated by the environment rather than genetics. Under optimal environmental conditions, populations consist of asexually reproducing females. When the environment shifts may be queued to reproduce sexually resulting in the production of male offspring and females carrying haploid eggs in sacs called ephippia (Mitchell 1999).
The purpose of this laboratory study is to examine the effects of increased water temperature on survivorship and reproduction. This study will help us characterize the magnitude of environmental change required to induce the onset of the sexual life cycle in . Because are known to be a sensitive environmental indicator species (Baker et al. 2000) and share similar structural and physiological features with many aquatic species, they serve as a good model for examining the effects of increasing water temperature on reproduction in a variety of aquatic invertebrates.
We hypothesized that populations reared in water temperatures ranging from 24-26 °C would have lower survivorship, higher male/female ratio among the offspring, and more female offspring carrying ephippia as compared with grown in water temperatures of 20-22°C. To test this hypothesis we reared populations in tanks containing water at either 24 +/- 2°C or 20 +/- 2°C. Over 10 days, we monitored survivorship, determined the sex of the offspring, and counted the number of female offspring containing ephippia.
Comments:
Background information
· Opening paragraph provides good focus immediately. The study organism, gender switching response, and temperature influence are mentioned in the first sentence. Although it does a good job documenting average lake water temperature and changes due to industrial run-off, it fails to make an argument that the 15% increase in lake temperature could be considered “extreme” temperature change.
· The study question is nicely embedded within relevant, well-cited background information. Alternatively, it could be stated as the first sentence in the introduction, or after all background information has been discussed before the hypothesis.
Rationale
· Good. Well-defined purpose for study; to examine the degree of environmental change necessary to induce the Daphnia sexual life
cycle.
How will introductions be evaluated? The following is part of the rubric we will be using to evaluate your papers.
0 = inadequate (C, D or F) | 1 = adequate (BC) | 2 = good (B) | 3 = very good (AB) | 4 = excellent (A) | |
Introduction BIG PICTURE: Did the Intro convey why experiment was performed and what it was designed to test?
| Introduction provides little to no relevant information. (This often results in a hypothesis that “comes out of nowhere.”) | Many key components are very weak or missing; those stated are unclear and/or are not stated concisely. Weak/missing components make it difficult to follow the rest of the paper. e.g., background information is not focused on a specific question and minimal biological rationale is presented such that hypothesis isn’t entirely logical
| Covers most key components but could be done much more logically, clearly, and/or concisely. e.g., biological rationale not fully developed but still supports hypothesis. Remaining components are done reasonably well, though there is still room for improvement. | Concisely & clearly covers all but one key component (w/ exception of rationale; see left) clearly covers all key components but could be a little more concise and/or clear. e.g., has done a reasonably nice job with the Intro but fails to state the approach OR has done a nice job with Intro but has also included some irrelevant background information
| Clearly, concisely, & logically presents all key components: relevant & correctly cited background information, question, biological rationale, hypothesis, approach. |
So you have carefully written your research paper and probably ran it through your colleagues ten to fifteen times. While there are many elements to a good research article, one of the most important elements for your readers is the background of your study.
The background of your study will provide context to the information discussed throughout the research paper . Background information may include both important and relevant studies. This is particularly important if a study either supports or refutes your thesis.
The background of the study discusses your problem statement, rationale, and research questions. It links introduction to your research topic and ensures a logical flow of ideas. Thus, it helps readers understand your reasons for conducting the study.
The reader should be able to understand your topic and its importance. The length and detail of your background also depend on the degree to which you need to demonstrate your understanding of the topic. Paying close attention to the following questions will help you in writing background information:
Related: Ready with the background and searching for more information on journal ranking? Check this infographic on the SCImago Journal Rank today!
Is the research study unique for which additional explanation is needed? For instance, you may have used a completely new method
The structure of a background study in a research paper generally follows a logical sequence to provide context, justification, and an understanding of the research problem. It includes an introduction, general background, literature review , rationale , objectives, scope and limitations , significance of the study and the research hypothesis . Following the structure can provide a comprehensive and well-organized background for your research.
Here are the steps to effectively write a background of the study.
Determine the level of expertise of your target audience. Tailor the depth and complexity of your background information accordingly.
Define the research problem or question your study aims to address. Identify the significance of the problem within the broader context of the field.
Conduct a thorough literature review to understand what is already known in the area. Summarize key findings, theories, and concepts relevant to your research.
Integrate historical data if relevant to the research, as current issues often trace back to historical events.
Note any controversies or debates within the existing literature. Identify gaps , limitations, or unanswered questions that your research can address.
Choose the most critical elements to include in the background based on their relevance to your research problem. Prioritize information that helps build a strong foundation for your study.
Organize the background information in a logical sequence. Start with general context, move to specific theories and concepts, and then focus on the specific problem.
Clearly explain the unique aspects or contributions of your study. Emphasize why your research is different from or builds upon existing work.
Here are some extra tips to increase the quality of your research background:
Here is an example of a research background to help you understand better.
The above hypothetical example provides a research background, addresses the gap and highlights the potential outcome of the study; thereby aiding a better understanding of the proposed research.
Your introduction is different from your background in a number of ways.
Your background should begin with defining a topic and audience. It is important that you identify which topic you need to review and what your audience already knows about the topic. You should proceed by searching and researching the relevant literature. In this case, it is advisable to keep track of the search terms you used and the articles that you downloaded. It is helpful to use one of the research paper management systems such as Papers, Mendeley, Evernote, or Sente. Next, it is helpful to take notes while reading. Be careful when copying quotes verbatim and make sure to put them in quotation marks and cite the sources. In addition, you should keep your background focused but balanced enough so that it is relevant to a broader audience. Aside from these, your background should be critical, consistent, and logically structured.
Writing the background of your study should not be an overly daunting task. Many guides that can help you organize your thoughts as you write the background. The background of the study is the key to introduce your audience to your research topic and should be done with strong knowledge and thoughtful writing.
The background of a research paper typically ranges from one to two paragraphs, summarizing the relevant literature and context of the study. It should be concise, providing enough information to contextualize the research problem and justify the need for the study. Journal instructions about any word count limits should be kept in mind while deciding on the length of the final content.
The background of a research paper provides the context and relevant literature to understand the research problem, while the introduction also introduces the specific research topic, states the research objectives, and outlines the scope of the study. The background focuses on the broader context, whereas the introduction focuses on the specific research project and its objectives.
When writing the background for a study, start by providing a brief overview of the research topic and its significance in the field. Then, highlight the gaps in existing knowledge or unresolved issues that the study aims to address. Finally, summarize the key findings from relevant literature to establish the context and rationale for conducting the research, emphasizing the need and importance of the study within the broader academic landscape.
The background in a research paper is crucial as it sets the stage for the study by providing essential context and rationale. It helps readers understand the significance of the research problem and its relevance in the broader field. By presenting relevant literature and highlighting gaps, the background justifies the need for the study, building a strong foundation for the research and enhancing its credibility.
The presentation very informative
It is really educative. I love the workshop. It really motivated me into writing my first paper for publication.
an interesting clue here, thanks.
thanks for the answers.
Good and interesting explanation. Thanks
Thank you for good presentation.
Hi Adam, we are glad to know that you found our article beneficial
The background of the study is the key to introduce your audience to YOUR research topic.
Awesome. Exactly what i was looking forwards to 😉
Hi Maryam, we are glad to know that you found our resource useful.
my understanding of ‘Background of study’ has been elevated.
Hi Peter, we are glad to know that our article has helped you get a better understanding of the background in a research paper.
thanks to give advanced information
Hi Shimelis, we are glad to know that you found the information in our article beneficial.
When i was studying it is very much hard for me to conduct a research study and know the background because my teacher in practical research is having a research so i make it now so that i will done my research
Very informative……….Thank you.
The confusion i had before, regarding an introduction and background to a research work is now a thing of the past. Thank you so much.
Thanks for your help…
Thanks for your kind information about the background of a research paper.
Thanks for the answer
Very informative. I liked even more when the difference between background and introduction was given. I am looking forward to learning more from this site. I am in Botswana
Hello, I am Benoît from Central African Republic. Right now I am writing down my research paper in order to get my master degree in British Literature. Thank you very much for posting all this information about the background of the study. I really appreciate. Thanks!
The write up is quite good, detailed and informative. Thanks a lot. The article has certainly enhanced my understanding of the topic.
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APA Background Research Paper . Science Fair Background Research Process: 1. You will need 60 index cards 2. You will make a list of 20 questions about your topic and then find answers for each of your 20 questions from 3…
Typing The APA Background Research Paper – Science Fair Background Research Process:1. You will need 60 index cards2. You will make a list of 20 questions about your topic and then find answers for each of your 20 questions from 3 different sources: Books, Internet, and Encyclopedias3. You will then write each of your 20 questions on one side of the index cards; one per index card. Repeat this a total of three times so that you have 60 index cards. IE: Topic: Sun Spots Question #1 “What causes sun spots?
Video advice: How to Write the Background of the Study in Research (Part 1). See Links Below for Parts 2, 3, and 4
Full transcript of the video lecture on \”How to Write the Background of the Study Part 1\” is available at:
Acaciawood Prep :: Science Fair – Acaciawood School 5th-8th grade students participate in an annual Science Fair competition. Projects are prepared throughout the fall semester and the first part of the spring semester for a competition in the early spring. The top projects in each grade of the school’s competition compete at the Orange County Science and Engineering Fair. Winners at the county level move on to the State Science Fair competition.
Science projects for school students range from simple setups and graphs to more complex fair-style displays or even extended activities. Students and teachers can complete some projects directly in the classroom, while others may need some at-home care to finish. Whether you (or your child or student) create an …
. . Science projects for college students vary from simple setups and graphs to more complicated fair-style displays or perhaps extended activities. Students and teachers can complete some projects directly within the classroom, while some may require some at-homecare to complete. Regardless of whether you (or perhaps your child or student) create a more sophisticated activity or something like that more fundamental, all science projects should contain history or perhaps a purpose statement. Explore this short article What’s History? Additional Information to incorporate Keeping History Finding History 1 What’s History? Science project history includes all research that you simply conduct prior to starting the game. For instance, should you design a task about how acids and bases react when mixed together, the backdrop section should contain specific info on acids, bases, litmus tests, chemical formulas, solutions, molecules and reactions.
Video advice: Challenge A Science Fair part 2… Research Question, Background Research, and Hypothesis
Video advice: Introduction to Science Fair Background Research Paper
The purpose of the Background Research Report is for you to gain knowledge about your Science Fair Project topic. This way you will be able to interpret the results of your experiment and draw conclusions based on the previous knowledge you gained by writing this report.
The background should be written as a summary of your interpretation of previous research and what your study proposes to accomplish .... How to avoid common mistakes in writing the background
Science project background information includes all research that you conduct before beginning the activity .
The background study for a thesis includes a review of the area being researched, current information surrounding the issue, previous studies on the issue, and relevant history on the issue. Ideally, the study should effectively set forth the history and background information on your thesis problem.
Background information is often provided after the hook, or opening statement that is used to grab the reader's attention. ... Examples of Background Information: In his inaugural speech at Rice University, John F. Kennedy spoke about the space race and going to the moon.
Science atlas, our goal is to spark the curiosity that exists in all of us. We invite readers to visit us daily, explore topics of interest, and gain new perspectives along the way.
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Published on May 20, 2021 by Pritha Bhandari . Revised on July 23, 2023.
A lab report conveys the aim, methods, results, and conclusions of a scientific experiment. The main purpose of a lab report is to demonstrate your understanding of the scientific method by performing and evaluating a hands-on lab experiment. This type of assignment is usually shorter than a research paper .
Lab reports are commonly used in science, technology, engineering, and mathematics (STEM) fields. This article focuses on how to structure and write a lab report.
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Structuring a lab report, introduction, other interesting articles, frequently asked questions about lab reports.
The sections of a lab report can vary between scientific fields and course requirements, but they usually contain the purpose, methods, and findings of a lab experiment .
Each section of a lab report has its own purpose.
Although most lab reports contain these sections, some sections can be omitted or combined with others. For example, some lab reports contain a brief section on research aims instead of an introduction, and a separate conclusion is not always required.
If you’re not sure, it’s best to check your lab report requirements with your instructor.
Professional editors proofread and edit your paper by focusing on:
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Your title provides the first impression of your lab report – effective titles communicate the topic and/or the findings of your study in specific terms.
Create a title that directly conveys the main focus or purpose of your study. It doesn’t need to be creative or thought-provoking, but it should be informative.
An abstract condenses a lab report into a brief overview of about 150–300 words. It should provide readers with a compact version of the research aims, the methods and materials used, the main results, and the final conclusion.
Think of it as a way of giving readers a preview of your full lab report. Write the abstract last, in the past tense, after you’ve drafted all the other sections of your report, so you’ll be able to succinctly summarize each section.
To write a lab report abstract, use these guiding questions:
Nitrogen is a necessary nutrient for high quality plants. Tomatoes, one of the most consumed fruits worldwide, rely on nitrogen for healthy leaves and stems to grow fruit. This experiment tested whether nitrogen levels affected tomato plant height in a controlled setting. It was expected that higher levels of nitrogen fertilizer would yield taller tomato plants.
Levels of nitrogen fertilizer were varied between three groups of tomato plants. The control group did not receive any nitrogen fertilizer, while one experimental group received low levels of nitrogen fertilizer, and a second experimental group received high levels of nitrogen fertilizer. All plants were grown from seeds, and heights were measured 50 days into the experiment.
The effects of nitrogen levels on plant height were tested between groups using an ANOVA. The plants with the highest level of nitrogen fertilizer were the tallest, while the plants with low levels of nitrogen exceeded the control group plants in height. In line with expectations and previous findings, the effects of nitrogen levels on plant height were statistically significant. This study strengthens the importance of nitrogen for tomato plants.
Your lab report introduction should set the scene for your experiment. One way to write your introduction is with a funnel (an inverted triangle) structure:
Begin by providing background information on your research topic and explaining why it’s important in a broad real-world or theoretical context. Describe relevant previous research on your topic and note how your study may confirm it or expand it, or fill a gap in the research field.
This lab experiment builds on previous research from Haque, Paul, and Sarker (2011), who demonstrated that tomato plant yield increased at higher levels of nitrogen. However, the present research focuses on plant height as a growth indicator and uses a lab-controlled setting instead.
Next, go into detail on the theoretical basis for your study and describe any directly relevant laws or equations that you’ll be using. State your main research aims and expectations by outlining your hypotheses .
Based on the importance of nitrogen for tomato plants, the primary hypothesis was that the plants with the high levels of nitrogen would grow the tallest. The secondary hypothesis was that plants with low levels of nitrogen would grow taller than plants with no nitrogen.
Your introduction doesn’t need to be long, but you may need to organize it into a few paragraphs or with subheadings such as “Research Context” or “Research Aims.”
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A lab report Method section details the steps you took to gather and analyze data. Give enough detail so that others can follow or evaluate your procedures. Write this section in the past tense. If you need to include any long lists of procedural steps or materials, place them in the Appendices section but refer to them in the text here.
You should describe your experimental design, your subjects, materials, and specific procedures used for data collection and analysis.
Briefly note whether your experiment is a within-subjects or between-subjects design, and describe how your sample units were assigned to conditions if relevant.
A between-subjects design with three groups of tomato plants was used. The control group did not receive any nitrogen fertilizer. The first experimental group received a low level of nitrogen fertilizer, while the second experimental group received a high level of nitrogen fertilizer.
Describe human subjects in terms of demographic characteristics, and animal or plant subjects in terms of genetic background. Note the total number of subjects as well as the number of subjects per condition or per group. You should also state how you recruited subjects for your study.
List the equipment or materials you used to gather data and state the model names for any specialized equipment.
List of materials
35 Tomato seeds
15 plant pots (15 cm tall)
Light lamps (50,000 lux)
Nitrogen fertilizer
Measuring tape
Describe your experimental settings and conditions in detail. You can provide labelled diagrams or images of the exact set-up necessary for experimental equipment. State how extraneous variables were controlled through restriction or by fixing them at a certain level (e.g., keeping the lab at room temperature).
Light levels were fixed throughout the experiment, and the plants were exposed to 12 hours of light a day. Temperature was restricted to between 23 and 25℃. The pH and carbon levels of the soil were also held constant throughout the experiment as these variables could influence plant height. The plants were grown in rooms free of insects or other pests, and they were spaced out adequately.
Your experimental procedure should describe the exact steps you took to gather data in chronological order. You’ll need to provide enough information so that someone else can replicate your procedure, but you should also be concise. Place detailed information in the appendices where appropriate.
In a lab experiment, you’ll often closely follow a lab manual to gather data. Some instructors will allow you to simply reference the manual and state whether you changed any steps based on practical considerations. Other instructors may want you to rewrite the lab manual procedures as complete sentences in coherent paragraphs, while noting any changes to the steps that you applied in practice.
If you’re performing extensive data analysis, be sure to state your planned analysis methods as well. This includes the types of tests you’ll perform and any programs or software you’ll use for calculations (if relevant).
First, tomato seeds were sown in wooden flats containing soil about 2 cm below the surface. Each seed was kept 3-5 cm apart. The flats were covered to keep the soil moist until germination. The seedlings were removed and transplanted to pots 8 days later, with a maximum of 2 plants to a pot. Each pot was watered once a day to keep the soil moist.
The nitrogen fertilizer treatment was applied to the plant pots 12 days after transplantation. The control group received no treatment, while the first experimental group received a low concentration, and the second experimental group received a high concentration. There were 5 pots in each group, and each plant pot was labelled to indicate the group the plants belonged to.
50 days after the start of the experiment, plant height was measured for all plants. A measuring tape was used to record the length of the plant from ground level to the top of the tallest leaf.
In your results section, you should report the results of any statistical analysis procedures that you undertook. You should clearly state how the results of statistical tests support or refute your initial hypotheses.
The main results to report include:
The mean heights of the plants in the control group, low nitrogen group, and high nitrogen groups were 20.3, 25.1, and 29.6 cm respectively. A one-way ANOVA was applied to calculate the effect of nitrogen fertilizer level on plant height. The results demonstrated statistically significant ( p = .03) height differences between groups.
Next, post-hoc tests were performed to assess the primary and secondary hypotheses. In support of the primary hypothesis, the high nitrogen group plants were significantly taller than the low nitrogen group and the control group plants. Similarly, the results supported the secondary hypothesis: the low nitrogen plants were taller than the control group plants.
These results can be reported in the text or in tables and figures. Use text for highlighting a few key results, but present large sets of numbers in tables, or show relationships between variables with graphs.
You should also include sample calculations in the Results section for complex experiments. For each sample calculation, provide a brief description of what it does and use clear symbols. Present your raw data in the Appendices section and refer to it to highlight any outliers or trends.
The Discussion section will help demonstrate your understanding of the experimental process and your critical thinking skills.
In this section, you can:
Interpreting your results involves clarifying how your results help you answer your main research question. Report whether your results support your hypotheses.
Compare your findings with other research and explain any key differences in findings.
An effective Discussion section will also highlight the strengths and limitations of a study.
When describing limitations, use specific examples. For example, if random error contributed substantially to the measurements in your study, state the particular sources of error (e.g., imprecise apparatus) and explain ways to improve them.
The results support the hypothesis that nitrogen levels affect plant height, with increasing levels producing taller plants. These statistically significant results are taken together with previous research to support the importance of nitrogen as a nutrient for tomato plant growth.
However, unlike previous studies, this study focused on plant height as an indicator of plant growth in the present experiment. Importantly, plant height may not always reflect plant health or fruit yield, so measuring other indicators would have strengthened the study findings.
Another limitation of the study is the plant height measurement technique, as the measuring tape was not suitable for plants with extreme curvature. Future studies may focus on measuring plant height in different ways.
The main strengths of this study were the controls for extraneous variables, such as pH and carbon levels of the soil. All other factors that could affect plant height were tightly controlled to isolate the effects of nitrogen levels, resulting in high internal validity for this study.
Your conclusion should be the final section of your lab report. Here, you’ll summarize the findings of your experiment, with a brief overview of the strengths and limitations, and implications of your study for further research.
Some lab reports may omit a Conclusion section because it overlaps with the Discussion section, but you should check with your instructor before doing so.
If you want to know more about AI for academic writing, AI tools, or fallacies make sure to check out some of our other articles with explanations and examples or go directly to our tools!
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A lab report conveys the aim, methods, results, and conclusions of a scientific experiment . Lab reports are commonly assigned in science, technology, engineering, and mathematics (STEM) fields.
The purpose of a lab report is to demonstrate your understanding of the scientific method with a hands-on lab experiment. Course instructors will often provide you with an experimental design and procedure. Your task is to write up how you actually performed the experiment and evaluate the outcome.
In contrast, a research paper requires you to independently develop an original argument. It involves more in-depth research and interpretation of sources and data.
A lab report is usually shorter than a research paper.
The sections of a lab report can vary between scientific fields and course requirements, but it usually contains the following:
The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.
In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.
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Bhandari, P. (2023, July 23). How To Write A Lab Report | Step-by-Step Guide & Examples. Scribbr. Retrieved August 8, 2024, from https://www.scribbr.com/academic-writing/lab-report/
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Background information identifies and describes the history and nature of a well-defined research problem with reference to the existing literature. The background information should indicate the root of the problem being studied, appropriate context of the problem in relation to theory, research, and/or practice , its scope, and the extent to which previous studies have successfully investigated the problem, noting, in particular, where gaps exist that your study attempts to address.
Providing background information in the introduction of a research paper serves as a bridge that links the reader to the topic of your study . Precisely how long and in-depth this bridge should be is largely dependent upon how much information you think the reader will need to know in order to fully understand the topic being discussed and to appreciate why the issues you are investigating are important.
From another perspective, the length and detail of background information also depends on the degree to which you need to demonstrate to your professor how much you understand the research problem. Keep this in mind because providing pertinent background information can be an effective way to demonstrate that you have a clear grasp of key issues and concepts underpinning your overall study. Don't try to show off, though! And, avoid stating the obvious.
The structure and writing style of your background information can vary depending upon the complexity of your research and/or the nature of the assignment. Given this, here are some questions to consider while writing this part of your introduction :
Almost all introductions to a research problem require some contextualizing, but the scope and breadth of background information varies depending on your assumption about the reader's level of prior knowledge . Despite this assessment, however, background information should be brief and succinct; save any elaboration of critical points or in-depth discussion of key issues for the literature review section of your paper.
Background of the Problem Section: What do you Need to Consider? Anonymous. Harvard University; Hopkins, Will G. How to Write a Research Paper . SPORTSCIENCE, Perspectives/Research Resources. Department of Physiology and School of Physical Education, University of Otago, 1999; Green, L. H. How to Write the Background/Introduction Section . Physics 499 Powerpoint slides. University of Illinois; Woodall, W. Gill. Writing the Background and Significance Section . Senior Research Scientist and Professor of Communication. Center on Alcoholism, Substance Abuse, and Addictions. University of New Mexico.
Background Information vs. the Literature Review
Incorporating background information into the introduction is intended to provide the reader with critical information about the topic being studied, such as, highlighting and expanding upon foundational studies conducted in the past, describing important historical events that inform why and in what ways the research problem exists, or defining key components of your study [concepts, people, places, things]. Although in social sciences research introductory background information can often blend into the literature review portion of the paper, basic background information should not be considered a substitute for a comprehensive review and synthesis of relevant research literature.
Hart, Cris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage, 1998.
Coastal Marine Research (BIS 124) was taught at Bodega Marine Laboratory during Summer Session I by Professor Eric Sanford. Undergraduate students in this course received training in all aspects of the research process and then put their new skills to work during independent research projects conducted in teams of two. Students shared their research findings via a scientific talk delivered at the BML Student Research Symposium on August 1, and a final paper archived in the Cadet Hand Library at BML.
Students also developed science communication skills for engaging non-scientists. Students produced 3-minute films to highlight their research for general audiences and premiered their work at a Film Festival at Bodega Marine Lab on July 30. These films are now available to watch below.
Filmmakers: Dorado Barrios & Charlise Cambronero
Filmmakers: Natalia Goston & Christian Long
Filmmakers: Jasper Jacobs & Makayla Peixoto
Filmmakers: Emily Horn & Ava Hurley
Filmmaker: Indi Mejia
Filmmakers: Nathan Cole & Kaden Stone
Filmmakers: Zoe Roberson & Darien Valdez
Filmmakers: Jamison Nagle & Austin Wang
Filmmakers: Michell Valencia-Ortega & Kristen Wojciechowski
Filmmaker: Max Mao
Data science students William Hou and Ashley Yung were recently recognized for “demonstrated ability, leadership, and professional promise” by the Orange County / Long Beach chapter of the American Statistical Association . Both graduated from UC Irvine’s Donald Bren School of Information and Computer Sciences (ICS) in June of this year and while their paths to statistics differed, they share a similar passion for the field.
Hou’s interest in data science was first sparked in high school after analyzing competition data on the robotics team. Yung, who started out as a psychological sciences major, realized she had an interest in statistical methods and quantitative measures while conducting experiments in the social sciences setting.
“Both Ashley and William have been not only excellent students but also great peers,” says Associate Professor of Teaching Mine Dogucu , who nominated each for the student award. She has been particularly impressed by their outreach to other students through the Data@UCI student organization. “Under their leadership, Data@UCI has thrived.”
Hou and a few friends founded Data@UCI in 2022, and he served as club president in 2022-23. “In the years since its founding, our club has grown to reach thousands of students, and we’ve established ourselves as the premier club for all things data at UCI,” says Hou. “I even leveraged my experience planning software hackathons to organize UCI’s very first data hackathon , bringing together hundreds of data enthusiasts around Southern California to learn together in a competitive setting.”
The hackathon continued this year, with help from Yung, who served as co-director of community development. In that role, she worked with other Data@UCI leaders to offer workshops, professional panels and speaker events to help students improve their analytical skills. Yung held a similar role in the Women in Information and Computer Sciences ( WICS ) student organization at UCI as well.
“In both organizations, I was an active club member dedicated to fostering community building and outreach,” says Yung. “I had the opportunity to meet and develop relationships with a lot of intelligent and passionate people.”
Yung stresses that her peers are also deserving of recognition. “I was extremely grateful to be recognized by the Orange County / Long Beach American Statistical Association [but] I also want to acknowledge all of my fellow data science peers at UCI, whom I believe are just as deserving of this award,” she says. “I am really fortunate to have attended UCI, where I’ve been able to meet so many inspiring people.”
As Hou and Yung start their data science careers, they do so with a wealth of knowledge and experience. Hou worked with Jonathan Watanabe in the School of Pharmaceutical Sciences, analyzing real-world medical data to inform clinical care and improve patient outcomes. He also completed an internship at Atlassian, where he is now employed and will be contributing insights to its product and marketing divisions.
Yung interned at JP Morgan Chase, gaining hands-on experience working on projects related to automation and time-series forecasting. She also emphasizes the practical experience gained through her capstone course. “I really enjoyed the final capstone project!” she says. “With guidance from our professors and corporate partners, we were able to build a project from scratch and present it at the ICS Project Expo . Overall, it was a great experience.” The two-quarter capstone course is required of all data science majors, ensuring students learn how to apply statistical and computational principles to large-scale real-world data analysis problems.
Hou also highlights UCI’s focus on practical application. “I appreciate how UCI’s data science major puts emphasis on both the computer science and statistics aspects of the field,” he says. “Many other programs focus on one more than the other, which often leads to critical skills employers are looking for falling between the gaps. Though you should know the theory behind any analysis, it is equally important to understand the tools used to put them into practice.”
This is one of the benefits of UCI offering its data science major through the School of ICS, which houses both the Department of Computer Science and the Department of Statistics. It fosters an environment of interdisciplinary collaboration.
“I believe I speak for faculty from both departments,” says Docugu, “when I say we cannot wait to see these two students taking over the data science world as UCI alumni.”
— Shani Murray
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Implementation Science volume 19 , Article number: 59 ( 2024 ) Cite this article
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The implementation of clinical practice guidelines (CPGs) is a cyclical process in which the evaluation stage can facilitate continuous improvement. Implementation science has utilized theoretical approaches, such as models and frameworks, to understand and address this process. This article aims to provide a comprehensive overview of the models and frameworks used to assess the implementation of CPGs.
A systematic review was conducted following the Cochrane methodology, with adaptations to the "selection process" due to the unique nature of this review. The findings were reported following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. Electronic databases were searched from their inception until May 15, 2023. A predetermined strategy and manual searches were conducted to identify relevant documents from health institutions worldwide. Eligible studies presented models and frameworks for assessing the implementation of CPGs. Information on the characteristics of the documents, the context in which the models were used (specific objectives, level of use, type of health service, target group), and the characteristics of each model or framework (name, domain evaluated, and model limitations) were extracted. The domains of the models were analyzed according to the key constructs: strategies, context, outcomes, fidelity, adaptation, sustainability, process, and intervention. A subgroup analysis was performed grouping models and frameworks according to their levels of use (clinical, organizational, and policy) and type of health service (community, ambulatorial, hospital, institutional). The JBI’s critical appraisal tools were utilized by two independent researchers to assess the trustworthiness, relevance, and results of the included studies.
Database searches yielded 14,395 studies, of which 80 full texts were reviewed. Eight studies were included in the data analysis and four methodological guidelines were additionally included from the manual search. The risk of bias in the studies was considered non-critical for the results of this systematic review. A total of ten models/frameworks for assessing the implementation of CPGs were found. The level of use was mainly policy, the most common type of health service was institutional, and the major target group was professionals directly involved in clinical practice. The evaluated domains differed between the models and there were also differences in their conceptualization. All the models addressed the domain "Context", especially at the micro level (8/12), followed by the multilevel (7/12). The domains "Outcome" (9/12), "Intervention" (8/12), "Strategies" (7/12), and "Process" (5/12) were frequently addressed, while "Sustainability" was found only in one study, and "Fidelity/Adaptation" was not observed.
The use of models and frameworks for assessing the implementation of CPGs is still incipient. This systematic review may help stakeholders choose or adapt the most appropriate model or framework to assess CPGs implementation based on their specific health context.
PROSPERO (International Prospective Register of Systematic Reviews) registration number: CRD42022335884. Registered on June 7, 2022.
Peer Review reports
Although the number of theoretical approaches has grown in recent years, there are still important gaps to be explored in the use of models and frameworks to assess the implementation of clinical practice guidelines (CPGs). This systematic review aims to contribute knowledge to overcome these gaps.
Despite the great advances in implementation science, evaluating the implementation of CPGs remains a challenge, and models and frameworks could support improvements in this field.
This study demonstrates that the available models and frameworks do not cover all characteristics and domains necessary for a complete evaluation of CPGs implementation.
The presented findings contribute to the field of implementation science, encouraging debate on choices and adaptations of models and frameworks for implementation research and evaluation.
Substantial investments have been made in clinical research and development in recent decades, increasing the medical knowledge base and the availability of health technologies [ 1 ]. The use of clinical practice guidelines (CPGs) has increased worldwide to guide best health practices and to maximize healthcare investments. A CPG can be defined as "any formal statements systematically developed to assist practitioner and patient decisions about appropriate health care for specific clinical circumstances" [ 2 ] and has the potential to improve patient care by promoting interventions of proven benefit and discouraging ineffective interventions. Furthermore, they can promote efficiency in resource allocation and provide support for managers and health professionals in decision-making [ 3 , 4 ].
However, having a quality CPG does not guarantee that the expected health benefits will be obtained. In fact, putting these devices to use still presents a challenge for most health services across distinct levels of government. In addition to the development of guidelines with high methodological rigor, those recommendations need to be available to their users; these recommendations involve the diffusion and dissemination stages, and they need to be used in clinical practice (implemented), which usually requires behavioral changes and appropriate resources and infrastructure. All these stages involve an iterative and complex process called implementation, which is defined as the process of putting new practices within a setting into use [ 5 , 6 ].
Implementation is a cyclical process, and the evaluation is one of its key stages, which allows continuous improvement of CPGs development and implementation strategies. It consists of verifying whether clinical practice is being performed as recommended (process evaluation or formative evaluation) and whether the expected results and impact are being reached (summative evaluation) [ 7 , 8 , 9 ]. Although the importance of the implementation evaluation stage has been recognized, research on how these guidelines are implemented is scarce [ 10 ]. This paper focused on the process of assessing CPGs implementation.
To understand and improve this complex process, implementation science provides a systematic set of principles and methods to integrate research findings and other evidence-based practices into routine practice and improve the quality and effectiveness of health services and care [ 11 ]. The field of implementation science uses theoretical approaches that have varying degrees of specificity based on the current state of knowledge and are structured based on theories, models, and frameworks [ 5 , 12 , 13 ]. A "Model" is defined as "a simplified depiction of a more complex world with relatively precise assumptions about cause and effect", and a "framework" is defined as "a broad set of constructs that organize concepts and data descriptively without specifying causal relationships" [ 9 ]. Although these concepts are distinct, in this paper, their use will be interchangeable, as they are typically like checklists of factors relevant to various aspects of implementation.
There are a variety of theoretical approaches available in implementation science [ 5 , 14 ], which can make choosing the most appropriate challenging [ 5 ]. Some models and frameworks have been categorized as "evaluation models" by providing a structure for evaluating implementation endeavors [ 15 ], even though theoretical approaches from other categories can also be applied for evaluation purposes because they specify concepts and constructs that may be operationalized and measured [ 13 ]. Two frameworks that can specify implementation aspects that should be evaluated as part of intervention studies are RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) [ 16 ] and PRECEDE-PROCEED (Predisposing, Reinforcing and Enabling Constructs in Educational Diagnosis and Evaluation-Policy, Regulatory, and Organizational Constructs in Educational and Environmental Development) [ 17 ]. Although the number of theoretical approaches has grown in recent years, the use of models and frameworks to evaluate the implementation of guidelines still seems to be a challenge.
This article aims to provide a complete map of the models and frameworks applied to assess the implementation of CPGs. The aim is also to subside debate and choices on models and frameworks for the research and evaluation of the implementation processes of CPGs and thus to facilitate the continued development of the field of implementation as well as to contribute to healthcare policy and practice.
A systematic review was conducted following the Cochrane methodology [ 18 ], with adaptations to the "selection process" due to the unique nature of this review (details can be found in the respective section). The review protocol was registered in PROSPERO (registration number: CRD42022335884) on June 7, 2022. This report adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 19 ] and a completed checklist is provided in Additional File 1.
The SDMO approach (Types of Studies, Types of Data, Types of Methods, Outcomes) [ 20 ] was utilized in this systematic review, outlined as follows:
All types of studies were considered for inclusion, as the assessment of CPG implementation can benefit from a diverse range of study designs, including randomized clinical trials/experimental studies, scale/tool development, systematic reviews, opinion pieces, qualitative studies, peer-reviewed articles, books, reports, and unpublished theses.
Studies were categorized based on their methodological designs, which guided the synthesis, risk of bias assessment, and presentation of results.
Study protocols and conference abstracts were excluded due to insufficient information for this review.
Studies that evaluated the implementation of CPGs either independently or as part of a multifaceted intervention.
Guidelines for evaluating CPG implementation.
Inclusion of CPGs related to any context, clinical area, intervention, and patient characteristics.
No restrictions were placed on publication date or language.
Exclusion criteria
General guidelines were excluded, as this review focused on 'models for evaluating clinical practice guidelines implementation' rather than the guidelines themselves.
Studies that focused solely on implementation determinants as barriers and enablers were excluded, as this review aimed to explore comprehensive models/frameworks.
Studies evaluating programs and policies were excluded.
Studies that only assessed implementation strategies (isolated actions) rather than the implementation process itself were excluded.
Studies that focused solely on the impact or results of implementation (summative evaluation) were excluded.
Not applicable.
All potential models or frameworks for assessing the implementation of CPG (evaluation models/frameworks), as well as their characteristics: name; specific objectives; levels of use (clinical, organizational, and policy); health system (public, private, or both); type of health service (community, ambulatorial, hospital, institutional, homecare); domains or outcomes evaluated; type of recommendation evaluated; context; limitations of the model.
Model was defined as "a deliberated simplification of a phenomenon on a specific aspect" [ 21 ].
Framework was defined as "structure, overview outline, system, or plan consisting of various descriptive categories" [ 21 ].
Models or frameworks used solely for the CPG development, dissemination, or implementation phase.
Models/frameworks used solely for assessment processes other than implementation, such as for the development or dissemination phase.
The systematic search was conducted on July 31, 2022 (and updated on May 15, 2023) in the following electronic databases: MEDLINE/PubMed, Centre for Reviews and Dissemination (CRD), the Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), EMBASE, Epistemonikos, Global Health, Health Systems Evidence, PDQ-Evidence, PsycINFO, Rx for Change (Canadian Agency for Drugs and Technologies in Health, CADTH), Scopus, Web of Science and Virtual Health Library (VHL). The Google Scholar database was used for the manual selection of studies (first 10 pages).
Additionally, hand searches were performed on the lists of references included in the systematic reviews and citations of the included studies, as well as on the websites of institutions working on CPGs development and implementation: Guidelines International Networks (GIN), National Institute for Health and Care Excellence (NICE; United Kingdom), World Health Organization (WHO), Centers for Disease Control and Prevention (CDC; USA), Institute of Medicine (IOM; USA), Australian Department of Health and Aged Care (ADH), Healthcare Improvement Scotland (SIGN), National Health and Medical Research Council (NHMRC; Australia), Queensland Health, The Joanna Briggs Institute (JBI), Ministry of Health and Social Policy of Spain, Ministry of Health of Brazil and Capes Theses and Dissertations Catalog.
The search strategy combined terms related to "clinical practice guidelines" (practice guidelines, practice guidelines as topic, clinical protocols), "implementation", "assessment" (assessment, evaluation), and "models, framework". The free term "monitoring" was not used because it was regularly related to clinical monitoring and not to implementation monitoring. The search strategies adapted for the electronic databases are presented in an additional file (see Additional file 2).
The results of the literature search from scientific databases, excluding the CRD database, were imported into Mendeley Reference Management software to remove duplicates. They were then transferred to the Rayyan platform ( https://rayyan.qcri.org ) [ 22 ] for the screening process. Initially, studies related to the "assessment of implementation of the CPG" were selected. The titles were first screened independently by two pairs of reviewers (first selection: four reviewers, NM, JB, SS, and JG; update: a pair of reviewers, NM and DG). The title screening was broad, including all potentially relevant studies on CPG and the implementation process. Following that, the abstracts were independently screened by the same group of reviewers. The abstract screening was more focused, specifically selecting studies that addressed CPG and the evaluation of the implementation process. In the next step, full-text articles were reviewed independently by a pair of reviewers (NM, DG) to identify those that explicitly presented "models" or "frameworks" for assessing the implementation of the CPG. Disagreements regarding the eligibility of studies were resolved through discussion and consensus, and by a third reviewer (JB) when necessary. One reviewer (NM) conducted manual searches, and the inclusion of documents was discussed with the other reviewers.
The selected studies were independently classified and evaluated according to their methodological designs by two investigators (NM and JG). This review employed JBI’s critical appraisal tools to assess the trustworthiness, relevance and results of the included studies [ 23 ] and these tools are presented in additional files (see Additional file 3 and Additional file 4). Disagreements were resolved by consensus or consultation with the other reviewers. Methodological guidelines and noncomparative and before–after studies were not evaluated because JBI does not have specific tools for assessing these types of documents. Although the studies were assessed for quality, they were not excluded on this basis.
The data was independently extracted by two reviewers (NM, DG) using a Microsoft Excel spreadsheet. Discrepancies were discussed and resolved by consensus. The following information was extracted:
Document characteristics : author; year of publication; title; study design; instrument of evaluation; country; guideline context;
Usage context of the models : specific objectives; level of use (clinical, organizational, and policy); type of health service (community, ambulatorial, hospital, institutional); target group (guideline developers, clinicians; health professionals; health-policy decision-makers; health-care organizations; service managers);
Model and framework characteristics : name, domain evaluated, and model limitations.
The set of information to be extracted, shown in the systematic review protocol, was adjusted to improve the organization of the analysis.
The "level of use" refers to the scope of the model used. "Clinical" was considered when the evaluation focused on individual practices, "organizational" when practices were within a health service institution, and "policy" when the evaluation was more systemic and covered different health services or institutions.
The "type of health service" indicated the category of health service where the model/framework was used (or can be used) to assess the implementation of the CPG, related to the complexity of healthcare. "Community" is related to primary health care; "ambulatorial" is related to secondary health care; "hospital" is related to tertiary health care; and "institutional" represented models/frameworks not specific to a particular type of health service.
The "target group" included stakeholders related to the use of the model/framework for evaluating the implementation of the CPG, such as clinicians, health professionals, guideline developers, health policy-makers, health organizations, and service managers.
The category "health system" (public, private, or both) mentioned in the systematic review protocol was not found in the literature obtained and was removed as an extraction variable. Similarly, the variables "type of recommendation evaluated" and "context" were grouped because the same information was included in the "guideline context" section of the study.
Some selected documents presented models or frameworks recognized by the scientific field, including some that were validated. However, some studies adapted the model to this context. Therefore, the domain analysis covered all models or frameworks domains evaluated by (or suggested for evaluation by) the document analyzed.
The results were tabulated using narrative synthesis with an aggregative approach, without meta-analysis, aiming to summarize the documents descriptively for the organization, description, interpretation and explanation of the study findings [ 24 , 25 ].
The model/framework domains evaluated in each document were studied according to Nilsen et al.’s constructs: "strategies", "context", "outcomes", "fidelity", "adaptation" and "sustainability". For this study, "strategies" were described as structured and planned initiatives used to enhance the implementation of clinical practice [ 26 ].
The definition of "context" varies in the literature. Despite that, this review considered it as the set of circumstances or factors surrounding a particular implementation effort, such as organizational support, financial resources, social relations and support, leadership, and organizational culture [ 26 , 27 ]. The domain "context" was subdivided according to the level of health care into "micro" (individual perspective), "meso" (organizational perspective), "macro" (systemic perspective), and "multiple" (when there is an issue involving more than one level of health care).
The "outcomes" domain was related to the results of the implementation process (unlike clinical outcomes) and was stratified according to the following constructs: acceptability, appropriateness, feasibility, adoption, cost, and penetration. All these concepts align with the definitions of Proctor et al. (2011), although we decided to separate "fidelity" and "sustainability" as independent domains similar to Nilsen [ 26 , 28 ].
"Fidelity" and "adaptation" were considered the same domain, as they are complementary pieces of the same issue. In this study, implementation fidelity refers to how closely guidelines are followed as intended by their developers or designers. On the other hand, adaptation involves making changes to the content or delivery of a guideline to better fit the needs of a specific context. The "sustainability" domain was defined as evaluations about the continuation or permanence over time of the CPG implementation.
Additionally, the domain "process" was utilized to address issues related to the implementation process itself, rather than focusing solely on the outcomes of the implementation process, as done by Wang et al. [ 14 ]. Furthermore, the "intervention" domain was introduced to distinguish aspects related to the CPG characteristics that can impact its implementation, such as the complexity of the recommendation.
A subgroup analysis was performed with models and frameworks categorized based on their levels of use (clinical, organizational, and policy) and the type of health service (community, ambulatorial, hospital, institutional) associated with the CPG. The goal is to assist stakeholders (politicians, clinicians, researchers, or others) in selecting the most suitable model for evaluating CPG implementation based on their specific health context.
Database searches yielded 26,011 studies, of which 107 full texts were reviewed. During the full-text review, 99 articles were excluded: 41 studies did not mention a model or framework for assessing the implementation of the CPG, 31 studies evaluated only implementation strategies (isolated actions) rather than the implementation process itself, and 27 articles were not related to the implementation assessment. Therefore, eight studies were included in the data analysis. The updated search did not reveal additional relevant studies. The main reason for study exclusion was that they did not use models or frameworks to assess CPG implementation. Additionally, four methodological guidelines were included from the manual search (Fig. 1 ).
PRISMA diagram. Acronyms: ADH—Australian Department of Health, CINAHL—Cumulative Index to Nursing and Allied Health Literature, CDC—Centers for Disease Control and Prevention, CRD—Centre for Reviews and Dissemination, GIN—Guidelines International Networks, HSE—Health Systems Evidence, IOM—Institute of Medicine, JBI—The Joanna Briggs Institute, MHB—Ministry of Health of Brazil, NICE—National Institute for Health and Care Excellence, NHMRC—National Health and Medical Research Council, MSPS – Ministerio de Sanidad Y Política Social (Spain), SIGN—Scottish Intercollegiate Guidelines Network, VHL – Virtual Health Library, WHO—World Health Organization. Legend: Reason A –The study evaluated only implementation strategies (isolated actions) rather than the implementation process itself. Reason B – The study did not mention a model or framework for assessing the implementation of the intervention. Reason C – The study was not related to the implementation assessment. Adapted from Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. https://doi.org/10.1136/bmj.n71 . For more information, visit:
According to the JBI’s critical appraisal tools, the overall assessment of the studies indicates their acceptance for the systematic review.
The cross-sectional studies lacked clear information regarding "confounding factors" or "strategies to address confounding factors". This was understandable given the nature of the study, where such details are not typically included. However, the reviewers did not find this lack of information to be critical, allowing the studies to be included in the review. The results of this methodological quality assessment can be found in an additional file (see Additional file 5).
In the qualitative studies, there was some ambiguity regarding the questions: "Is there a statement locating the researcher culturally or theoretically?" and "Is the influence of the researcher on the research, and vice versa, addressed?". However, the reviewers decided to include the studies and deemed the methodological quality sufficient for the analysis in this article, based on the other information analyzed. The results of this methodological quality assessment can be found in an additional file (see Additional file 6).
The documents were directed to several continents: Australia/Oceania (4/12) [ 31 , 33 , 36 , 37 ], North America (4/12 [ 30 , 32 , 38 , 39 ], Europe (2/12 [ 29 , 35 ] and Asia (2/12) [ 34 , 40 ]. The types of documents were classified as cross-sectional studies (4/12) [ 29 , 32 , 34 , 38 ], methodological guidelines (4/12) [ 33 , 35 , 36 , 37 ], mixed methods studies (3/12) [ 30 , 31 , 39 ] or noncomparative studies (1/12) [ 40 ]. In terms of the instrument of evaluation, most of the documents used a survey/questionnaire (6/12) [ 29 , 30 , 31 , 32 , 34 , 38 ], while three (3/12) used qualitative instruments (interviews, group discussions) [ 30 , 31 , 39 ], one used a checklist [ 37 ], one used an audit [ 33 ] and three (3/12) did not define a specific instrument to measure [ 35 , 36 , 40 ].
Considering the clinical areas covered, most studies evaluated the implementation of nonspecific (general) clinical areas [ 29 , 33 , 35 , 36 , 37 , 40 ]. However, some studies focused on specific clinical contexts, such as mental health [ 32 , 38 ], oncology [ 39 ], fall prevention [ 31 ], spinal cord injury [ 30 ], and sexually transmitted infections [ 34 ].
Specific objectives.
All the studies highlighted the purpose of guiding the process of evaluating the implementation of CPGs, even if they evaluated CPGs from generic or different clinical areas.
The most common level of use of the models/frameworks identified to assess the implementation of CPGs was policy (6/12) [ 33 , 35 , 36 , 37 , 39 , 40 ]. In this level, the model is used in a systematic way to evaluate all the processes involved in CPGs implementation and is primarily related to methodological guidelines. This was followed by the organizational level of use (5/12) [ 30 , 31 , 32 , 38 , 39 ], where the model is used to evaluate the implementation of CPGs in a specific institution, considering its specific environment. Finally, the clinical level of use (2/12) [ 29 , 34 ] focuses on individual practice and the factors that can influence the implementation of CPGs by professionals.
Institutional services were predominant (5/12) [ 33 , 35 , 36 , 37 , 40 ] and included methodological guidelines and a study of model development and validation. Hospitals were the second most common type of health service (4/12) [ 29 , 30 , 31 , 34 ], followed by ambulatorial (2/12) [ 32 , 34 ] and community health services (1/12) [ 32 ]. Two studies did not specify which type of health service the assessment addressed [ 38 , 39 ].
The focus of the target group was professionals directly involved in clinical practice (6/12) [ 29 , 31 , 32 , 34 , 38 , 40 ], namely, health professionals and clinicians. Other less related stakeholders included guideline developers (2/12) [ 39 , 40 ], health policy decision makers (1/12) [ 39 ], and healthcare organizations (1/12) [ 39 ]. The target group was not defined in the methodological guidelines, although all the mentioned stakeholders could be related to these documents.
Models and frameworks for assessing the implementation of cpgs.
The Consolidated Framework for Implementation Research (CFIR) [ 31 , 38 ] and the Promoting Action on Research Implementation in Health Systems (PARiHS) framework [ 29 , 30 ] were the most commonly employed frameworks within the selected documents. The other models mentioned were: Goal commitment and implementation of practice guidelines framework [ 32 ]; Guideline to identify key indicators [ 35 ]; Guideline implementation checklist [ 37 ]; Guideline implementation evaluation tool [ 40 ]; JBI Implementation Framework [ 33 ]; Reach, effectiveness, adoption, implementation and maintenance (RE-AIM) framework [ 34 ]; The Guideline Implementability Framework [ 39 ] and an unnamed model [ 36 ].
The number of domains evaluated (or suggested for evaluation) by the documents varied between three and five, with the majority focusing on three domains. All the models addressed the domain "context", with a particular emphasis on the micro level of the health care context (8/12) [ 29 , 31 , 34 , 35 , 36 , 37 , 38 , 39 ], followed by the multilevel (7/12) [ 29 , 31 , 32 , 33 , 38 , 39 , 40 ], meso level (4/12) [ 30 , 35 , 39 , 40 ] and macro level (2/12) [ 37 , 39 ]. The "Outcome" domain was evaluated in nine models. Within this domain, the most frequently evaluated subdomain was "adoption" (6/12) [ 29 , 32 , 34 , 35 , 36 , 37 ], followed by "acceptability" (4/12) [ 30 , 32 , 35 , 39 ], "appropriateness" (3/12) [ 32 , 34 , 36 ], "feasibility" (3/12) [ 29 , 32 , 36 ], "cost" (1/12) [ 35 ] and "penetration" (1/12) [ 34 ]. Regarding the other domains, "Intervention" (8/12) [ 29 , 31 , 34 , 35 , 36 , 38 , 39 , 40 ], "Strategies" (7/12) [ 29 , 30 , 33 , 35 , 36 , 37 , 40 ] and "Process" (5/12) [ 29 , 31 , 32 , 33 , 38 ] were frequently addressed in the models, while "Sustainability" (1/12) [ 34 ] was only found in one model, and "Fidelity/Adaptation" was not observed. The domains presented by the models and frameworks and evaluated in the documents are shown in Table 2 .
Only two documents mentioned limitations in the use of the model or frameworks. These two studies reported limitations in the use of CFIR: "is complex and cumbersome and requires tailoring of the key variables to the specific context", and "this framework should be supplemented with other important factors and local features to achieve a sound basis for the planning and realization of an ongoing project" [ 31 , 38 ]. Limitations in the use of other models or frameworks are not reported.
Following the subgroup analysis (Table 3 ), five different models/frameworks were utilized at the policy level by institutional health services. These included the Guideline Implementation Evaluation Tool [ 40 ], the NHMRC tool (model name not defined) [ 36 ], the JBI Implementation Framework + GRiP [ 33 ], Guideline to identify key indicators [ 35 ], and the Guideline implementation checklist [ 37 ]. Additionally, the "Guideline Implementability Framework" [ 39 ] was implemented at the policy level without restrictions based on the type of health service. Regarding the organizational level, the models used varied depending on the type of service. The "Goal commitment and implementation of practice guidelines framework" [ 32 ] was applied in community and ambulatory health services, while "PARiHS" [ 29 , 30 ] and "CFIR" [ 31 , 38 ] were utilized in hospitals. In contexts where the type of health service was not defined, "CFIR" [ 31 , 38 ] and "The Guideline Implementability Framework" [ 39 ] were employed. Lastly, at the clinical level, "RE-AIM" [ 34 ] was utilized in ambulatory and hospital services, and PARiHS [ 29 , 30 ] was specifically used in hospital services.
This systematic review identified 10 models/ frameworks used to assess the implementation of CPGs in various health system contexts. These documents shared similar objectives in utilizing models and frameworks for assessment. The primary level of use was policy, the most common type of health service was institutional, and the main target group of the documents was professionals directly involved in clinical practice. The models and frameworks presented varied analytical domains, with sometimes divergent concepts used in these domains. This study is innovative in its emphasis on the evaluation stage of CPG implementation and in summarizing aspects and domains aimed at the practical application of these models.
The small number of documents contrasts with studies that present an extensive range of models and frameworks available in implementation science. The findings suggest that the use of models and frameworks to evaluate the implementation of CPGs is still in its early stages. Among the selected documents, there was a predominance of cross-sectional studies and methodological guidelines, which strongly influenced how the implementation evaluation was conducted. This was primarily done through surveys/questionnaires, qualitative methods (interviews, group discussions), and non-specific measurement instruments. Regarding the subject areas evaluated, most studies focused on a general clinical area, while others explored different clinical areas. This suggests that the evaluation of CPG implementation has been carried out in various contexts.
The models were chosen independently of the categories proposed in the literature, with their usage categorized for purposes other than implementation evaluation, as is the case with CFIR and PARiHS. This practice was described by Nilsen et al. who suggested that models and frameworks from other categories can also be applied for evaluation purposes because they specify concepts and constructs that may be operationalized and measured [ 14 , 15 , 42 , 43 ].
The results highlight the increased use of models and frameworks in evaluation processes at the policy level and institutional environments, followed by the organizational level in hospital settings. This finding contradicts a review that reported the policy level as an area that was not as well studied [ 44 ]. The use of different models at the institutional level is also emphasized in the subgroup analysis. This may suggest that the greater the impact (social, financial/economic, and organizational) of implementing CPGs, the greater the interest and need to establish well-defined and robust processes. In this context, the evaluation stage stands out as crucial, and the investment of resources and efforts to structure this stage becomes even more advantageous [ 10 , 45 ]. Two studies (16,7%) evaluated the implementation of CPGs at the individual level (clinical level). These studies stand out for their potential to analyze variations in clinical practice in greater depth.
In contrast to the level of use and type of health service most strongly indicated in the documents, with systemic approaches, the target group most observed was professionals directly involved in clinical practice. This suggests an emphasis on evaluating individual behaviors. This same emphasis is observed in the analysis of the models, in which there is a predominance of evaluating the micro level of the health context and the "adoption" subdomain, in contrast with the sub-use of domains such as "cost" and "process". Cassetti et al. observed the same phenomenon in their review, in which studies evaluating the implementation of CPGs mainly adopted a behavioral change approach to tackle those issues, without considering the influence of wider social determinants of health [ 10 ]. However, the literature widely reiterates that multiple factors impact the implementation of CPGs, and different actions are required to make them effective [ 6 , 46 , 47 ]. As a result, there is enormous potential for the development and adaptation of models and frameworks aimed at more systemic evaluation processes that consider institutional and organizational aspects.
In analyzing the model domains, most models focused on evaluating only some aspects of implementation (three domains). All models evaluated the "context", highlighting its significant influence on implementation [ 9 , 26 ]. Context is an essential effect modifier for providing research evidence to guide decisions on implementation strategies [ 48 ]. Contextualizing a guideline involves integrating research or other evidence into a specific circumstance [ 49 ]. The analysis of this domain was adjusted to include all possible contextual aspects, even if they were initially allocated to other domains. Some contextual aspects presented by the models vary in comprehensiveness, such as the assessment of the "timing and nature of stakeholder engagement" [ 39 ], which includes individual engagement by healthcare professionals and organizational involvement in CPG implementation. While the importance of context is universally recognized, its conceptualization and interpretation differ across studies and models. This divergence is also evident in other domains, consistent with existing literature [ 14 ]. Efforts to address this conceptual divergence in implementation science are ongoing, but further research and development are needed in this field [ 26 ].
The main subdomain evaluated was "adoption" within the outcome domain. This may be attributed to the ease of accessing information on the adoption of the CPG, whether through computerized system records, patient records, or self-reports from healthcare professionals or patients themselves. The "acceptability" subdomain pertains to the perception among implementation stakeholders that a particular CPG is agreeable, palatable or satisfactory. On the other hand, "appropriateness" encompasses the perceived fit, relevance or compatibility of the CPG for a specific practice setting, provider, or consumer, or its perceived fit to address a particular issue or problem [ 26 ]. Both subdomains are subjective and rely on stakeholders' interpretations and perceptions of the issue being analyzed, making them susceptible to reporting biases. Moreover, obtaining this information requires direct consultation with stakeholders, which can be challenging for some evaluation processes, particularly in institutional contexts.
The evaluation of the subdomains "feasibility" (the extent to which a CPG can be successfully used or carried out within a given agency or setting), "cost" (the cost impact of an implementation effort), and "penetration" (the extent to which an intervention or treatment is integrated within a service setting and its subsystems) [ 26 ] was rarely observed in the documents. This may be related to the greater complexity of obtaining information on these aspects, as they involve cross-cutting and multifactorial issues. In other words, it would be difficult to gather this information during evaluations with health practitioners as the target group. This highlights the need for evaluation processes of CPGs implementation involving multiple stakeholders, even if the evaluation is adjusted for each of these groups.
Although the models do not establish the "intervention" domain, we thought it pertinent in this study to delimit the issues that are intrinsic to CPGs, such as methodological quality or clarity in establishing recommendations. These issues were quite common in the models evaluated but were considered in other domains (e.g., in "context"). Studies have reported the importance of evaluating these issues intrinsic to CPGs [ 47 , 50 ] and their influence on the implementation process [ 51 ].
The models explicitly present the "strategies" domain, and its evaluation was usually included in the assessments. This is likely due to the expansion of scientific and practical studies in implementation science that involve theoretical approaches to the development and application of interventions to improve the implementation of evidence-based practices. However, these interventions themselves are not guaranteed to be effective, as reported in a previous review that showed unclear results indicating that the strategies had affected successful implementation [ 52 ]. Furthermore, model domains end up not covering all the complexity surrounding the strategies and their development and implementation process. For example, the ‘Guideline implementation evaluation tool’ evaluates whether guideline developers have designed and provided auxiliary tools to promote the implementation of guidelines [ 40 ], but this does not mean that these tools would work as expected.
The "process" domain was identified in the CFIR [ 31 , 38 ], JBI/GRiP [ 33 ], and PARiHS [ 29 ] frameworks. While it may be included in other domains of analysis, its distinct separation is crucial for defining operational issues when assessing the implementation process, such as determining if and how the use of the mentioned CPG was evaluated [ 3 ]. Despite its presence in multiple models, there is still limited detail in the evaluation guidelines, which makes it difficult to operationalize the concept. Further research is needed to better define the "process" domain and its connections and boundaries with other domains.
The domain of "sustainability" was only observed in the RE-AIM framework, which is categorized as an evaluation framework [ 34 ]. In its acronym, the letter M stands for "maintenance" and corresponds to the assessment of whether the user maintains use, typically longer than 6 months. The presence of this domain highlights the need for continuous evaluation of CPGs implementation in the short, medium, and long term. Although the RE-AIM framework includes this domain, it was not used in the questionnaire developed in the study. One probable reason is that the evaluation of CPGs implementation is still conducted on a one-off basis and not as a continuous improvement process. Considering that changes in clinical practices are inherent over time, evaluating and monitoring changes throughout the duration of the CPG could be an important strategy for ensuring its implementation. This is an emerging field that requires additional investment and research.
The "Fidelity/Adaptation" domain was not observed in the models. These emerging concepts involve the extent to which a CPG is being conducted exactly as planned or whether it is undergoing adjustments and adaptations. Whether or not there is fidelity or adaptation in the implementation of CPGs does not presuppose greater or lesser effectiveness; after all, some adaptations may be necessary to implement general CPGs in specific contexts. The absence of this domain in all the models and frameworks may suggest that they are not relevant aspects for evaluating implementation or that there is a lack of knowledge of these complex concepts. This may suggest difficulty in expressing concepts in specific evaluative questions. However, further studies are warranted to determine the comprehensiveness of these concepts.
It is important to note the customization of the domains of analysis, with some domains presented in the models not being evaluated in the studies, while others were complementarily included. This can be seen in Jeong et al. [ 34 ], where the "intervention" domain in the evaluation with the RE-AIM framework reinforced the aim of theoretical approaches such as guiding the process and not determining norms. Despite this, few limitations were reported for the models, suggesting that the use of models in these studies reflects the application of these models to defined contexts without a deep critical analysis of their domains.
This review has several limitations. First, only a few studies and methodological guidelines that explicitly present models and frameworks for assessing the implementation of CPGs have been found. This means that few alternative models could be analyzed and presented in this review. Second, this review adopted multiple analytical categories (e.g., level of use, health service, target group, and domains evaluated), whose terminology has varied enormously in the studies and documents selected, especially for the "domains evaluated" category. This difficulty in harmonizing the taxonomy used in the area has already been reported [ 26 ] and has significant potential to confuse. For this reason, studies and initiatives are needed to align understandings between concepts and, as far as possible, standardize them. Third, in some studies/documents, the information extracted was not clear about the analytical category. This required an in-depth interpretative process of the studies, which was conducted in pairs to avoid inappropriate interpretations.
This study contributes to the literature and clinical practice management by describing models and frameworks specifically used to assess the implementation of CPGs based on their level of use, type of health service, target group related to the CPG, and the evaluated domains. While there are existing reviews on the theories, frameworks, and models used in implementation science, this review addresses aspects not previously covered in the literature. This valuable information can assist stakeholders (such as politicians, clinicians, researchers, etc.) in selecting or adapting the most appropriate model to assess CPG implementation based on their health context. Furthermore, this study is expected to guide future research on developing or adapting models to assess the implementation of CPGs in various contexts.
The use of models and frameworks to evaluate the implementation remains a challenge. Studies should clearly state the level of model use, the type of health service evaluated, and the target group. The domains evaluated in these models may need adaptation to specific contexts. Nevertheless, utilizing models to assess CPGs implementation is crucial as they can guide a more thorough and systematic evaluation process, aiding in the continuous improvement of CPGs implementation. The findings of this systematic review offer valuable insights for stakeholders in selecting or adjusting models and frameworks for CPGs evaluation, supporting future theoretical advancements and research.
Abbreviations.
Australian Department of Health and Aged Care
Canadian Agency for Drugs and Technologies in Health
Centers for Disease Control and
Consolidated Framework for Implementation Research
Cumulative Index to Nursing and Allied Health Literature
Clinical practice guideline
Centre for Reviews and Dissemination
Guidelines International Networks
Getting Research into Practice
Health Systems Evidence
Institute of Medicine
The Joanna Briggs Institute
Ministry of Health of Brazil
Ministerio de Sanidad y Política Social
National Health and Medical Research Council
National Institute for Health and Care Excellence
Promoting action on research implementation in health systems framework
Predisposing, Reinforcing and Enabling Constructs in Educational Diagnosis and Evaluation-Policy, Regulatory, and Organizational Constructs in Educational and Environmental Development
Preferred Reporting Items for Systematic Reviews and Meta-Analyses
International Prospective Register of Systematic Reviews
Reach, effectiveness, adoption, implementation, and maintenance framework
Healthcare Improvement Scotland
United States of America
Virtual Health Library
World Health Organization
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Additional file 1: PRISMA checklist. Description of data: Completed PRISMA checklist used for reporting the results of this systematic review.
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Additional file 3: JBI’s critical appraisal tools for cross-sectional studies. Description of data: JBI’s critical appraisal tools to assess the trustworthiness, relevance, and results of the included studies. This is specific for cross-sectional studies.
Additional file 4: JBI’s critical appraisal tools for qualitative studies. Description of data: JBI’s critical appraisal tools to assess the trustworthiness, relevance, and results of the included studies. This is specific for qualitative studies.
Additional file 5: Methodological quality assessment results for cross-sectional studies. Description of data: Methodological quality assessment results for cross-sectional studies using JBI’s critical appraisal tools.
Additional file 6: Methodological quality assessment results for the qualitative studies. Description of data: Methodological quality assessment results for qualitative studies using JBI’s critical appraisal tools.
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It was much more accurate than primary care doctors using cognitive tests and CT scans. The findings could speed the quest for an affordable and accessible way to diagnose patients with memory problems.
By Pam Belluck
Scientists have made another major stride toward the long-sought goal of diagnosing Alzheimer’s disease with a simple blood test . On Sunday, a team of researchers reported that a blood test was significantly more accurate than doctors’ interpretation of cognitive tests and CT scans in signaling the condition.
The study , published Sunday in the journal JAMA, found that about 90 percent of the time the blood test correctly identified whether patients with memory problems had Alzheimer’s. Dementia specialists using standard methods that did not include expensive PET scans or invasive spinal taps were accurate 73 percent of the time, while primary care doctors using those methods got it right only 61 percent of the time.
“Not too long ago measuring pathology in the brain of a living human was considered just impossible,” said Dr. Jason Karlawish, a co-director of the Penn Memory Center at the University of Pennsylvania who was not involved in the research. “This study adds to the revolution that has occurred in our ability to measure what’s going on in the brain of living humans.”
The results, presented Sunday at the Alzheimer’s Association International Conference in Philadelphia, are the latest milestone in the search for affordable and accessible ways to diagnose Alzheimer’s, a disease that afflicts nearly seven million Americans and over 32 million people worldwide. Medical experts say the findings bring the field closer to a day when people might receive routine blood tests for cognitive impairment as part of primary care checkups, similar to the way they receive cholesterol tests.
“Now, we screen people with mammograms and PSA or prostate exams and other things to look for very early signs of cancer,” said Dr. Adam Boxer, a neurologist at the University of California, San Francisco, who was not involved in the study. “And I think we’re going to be doing the same thing for Alzheimer’s disease and hopefully other forms of neurodegeneration.”
In recent years, several blood tests have been developed for Alzheimer’s. They are currently used mostly to screen participants in clinical trials and by some specialists like Dr. Boxer to help pinpoint if a patient’s dementia is caused by Alzheimer’s or another condition.
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August 9, 2024
Dear Colleagues:
With this Dear Colleague Letter (DCL), the U.S. National Science Foundation's Directorate for Technology, Innovation and Partnerships (TIP) and the Department of Energy's Advanced Materials and Manufacturing Technologies Office (AMMTO) within the Office of Energy Efficiency and Renewable Energy (EERE) announce their intent to together establish a strategic leadership network on climate action engagement aimed at the transition from the linear economy of today to a circular economy of tomorrow, including one that will better integrate university research and community needs. With this investment, NSF and DOE aim to organize "Climate Action Ambassadors" into an effective national network of professionals to develop evidence-based models, program concepts and recommendations that share, implement, and scale university-community partnerships in this national priority area.
The expertise of the Ambassadors will span climate action strategies across all areas of science and engineering for mitigation (including circular design and circular economy principles), resiliency, and/or adaptation, resulting in a new multi-disciplinary, multi-sector Research Coordination Network (RCN) called a U niversity- C ommunity C limate A ction N etwork (UCCAN). Ambassadors will be geographically distributed and represent the diverse range of communities (e.g., cities, towns, tribal nations) as well as U.S. Institutions of Higher Education (IHEs), referred to in this DCL as colleges and universities (e.g., Land Grant, public, private, community, technical, Minority Serving Institutions) across the nation.
NSF and DOE will invest in one RCN proposal to develop new connections and collaborations to accelerate and elevate climate action through university-community partnerships and identify untapped resources to ensure environmental justice for unserved and under-served communities. This RCN-UCCAN will provide a framework for engaging students and participating local communities to co-create and scale successful proof-of-concept projects. The RCN-UCCAN also will create the "connective tissue" to build upon existing efforts and support collaborations and communication between campuses with governments, industry, and their surrounding communities to enable fast response, replication, and scaling of climate mitigation, resilience, and/or adaptation solutions nationally.
Ambassadors will build upon the momentum and findings of an initial meeting convened by NSF together with other federal departments, agencies, and offices on March 8-9, 2023. Specifically, that forum was a first step in an inclusive effort to marshal the strengths of campuses, including the nearly 20 million students enrolled in the U.S. post-secondary education system, to help address climate mitigation, resilience, and/or adaptation, environmental sustainability, and environmental justice, and encourage and support their innovation and entrepreneurship. Students seek access to the translational knowledge and skills to address issues related to climate and environment in their communities, which this RCN-UCCAN aims to uniquely make possible.
The RCN-UCCAN should aim to develop a national strategy connecting our colleges and universities with their communities to share knowledge and resources across a network that spans a range of institution types. Of particular interest is ensuring that everyone, regardless of location or affiliation, has the opportunity to address the goals outlined in this DCL. Researchers and practitioners across their communities are especially encouraged to communicate, collaborate and exchange information for action on climate mitigation, resilience and adaption through the RCN, including with international partners.
Proposals responding to this DCL should be prepared in accordance with the guidance contained in the RCN solicitation including the seven guidance items outlined in Section II. Program Description. When submitting the proposal, select the RCN solicitation and then direct the proposal to the Special Projects Program in the Division of Innovation and Technology Ecosystems (ITE) within the TIP directorate. Proposal titles should begin with "RCN-UCCAN:" followed by a substantive title. RCN-UCCAN proposals should be received by November 15, 2024 (due by 5 p.m. submitting organization's local time).
Proposals should include a network of named Ambassadors who will contribute a substantial portion (minimum of 20% with a greater commitment preferred) of their time to the RCN-UCCAN. For instance, coordinating sabbatical or other similar time away from normal duties is encouraged. Each Ambassador who is either a faculty or practitioner (such as a Director or Vice President of Sustainability) should form a substantive collaboration with a community Ambassador focused on climate action and facilitating interactions with students. Each named Ambassador should also enlist the help of one or more undergraduate students, graduate students, and/or post-doctoral fellows for a minimum of 10 hours per week. Additionally, to ensure co-design and co-creation of solutions, specific funding to support a community leader to serve as an Ambassador alongside the university Ambassador for a minimum of 20% of their time should be included. Further, proposals that show substantive reach and collaboration with K-12 networks in the participating communities/regions are highly encouraged.
For purposes of this DCL, any RCN-UCCAN proposal should address the proposer's ability to contribute to the following deliverables envisioned for the RCN-UCCAN:
The essential work of the Ambassadors will be to leverage existing and future climate solutions and strengthen the ties between the Universities and their respective communities as well as create a strategic network across all institutions to leverage best practices, build new collaborations, and share resources. The RCN-UCCAN thus formed will be the result of the efforts of the Ambassadors. It is further anticipated that the RCN-UCCAN network would grow each year with external funding from sources beyond NSF. Such growth can lead to a new set of Ambassadors to build upon, update, and/or execute the strategy to mitigate further climate change and build capacity for resilience and adaptation. In this way, the reach of coordination and cooperation across the US and internationally can be extended and sustained.
IMAGES
COMMENTS
How to write a science fair project research paper. Includes key areas for research and sample papers.
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Your science fair project may require a lab report or essay. This is a general outline showing how to prepare a report to document your research.
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The background of the study is placed at the beginning of a research paper. It provides the context, circumstances, and history that led to your research.
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background The expertise of the Ambassadors will span climate action strategies across all areas of science and engineering for mitigation (including circular design and circular economy principles), resiliency, and/or adaptation, resulting in a new multi-disciplinary, multi-sector Research Coordination Network (RCN) called a U niversity- C ...
Background research is necessary so that you know how to design and understand your experiment. To make a background research plan—a roadmap of the research questions you need to answer—follow these steps: 1. Identify the keywords in the question for your science fair project. Brainstorm additional keywords and concepts.
Background research is especially important for engineering design projects, because you can learn from the experience of others rather than blunder around and repeat their mistakes. To make a background research plan — a roadmap of the research questions you need to answer -- follow these steps: Identify questions to ask about your target ...