Paper 4: a systematic review on the use of logic models and frameworks for methodological conduct of evidence synthesis
Bibliographic record
Abstract
OBJECTIVES: To identify evidence syntheses of health interventions addressing racial health equity reporting the use of equity-focused frameworks and logic models. STUDY DESIGN AND SETTING: The search strategy included three sources; a search of three bibliographic databases to identify systematic reviews assessing interventions to improve racial health equity, semistructured interviews with diverse group and a targeted organization website searches (eg, National Institute of Health, United States Preventive Services Task Force) to identify relevant logic models and frameworks. The searches were conducted between January 1, 2020, and January 25, 2023. We used a qualitative approach to identify and describe key characteristics of equity-focused logic models and frameworks used in evidence syntheses. RESULTS: Of the 153 racial health equity-focused evidence syntheses identified, two explicitly used logic models to describe the intervention mechanism. We identified seven existing health equity frameworks from semistructured interviews and electronic search of key websites that were categorized by stated purpose as providing guidance for 1) research, 2) health policy, 3) digital health-care solutions, and 4) clinical preventive services. Two out of seven frameworks included guidance on integrating frameworks or logic models in evidence synthesis while the majority provided contextual information on how to define or consider race or racism as a structural determinant of health. CONCLUSION: There is limited use of logic models and frameworks in evidence syntheses addressing racial health equity. There is a need for more applied frameworks providing guidance for framing, conducting and interpreting findings of evidence syntheses addressing racial health equity. PLAIN LANGUAGE SUMMARY: The goal of this study was to find reviews of health programs that focus on improving racial health equity, and to see if they used special frameworks or models designed to address equity. To do this, we searched three major research databases, conducted interviews with a diverse group of people, and looked at relevant organization websites (like the National Institute of Health and the World Health Organization) between January 2020 and January 2023. We used a qualitative approach to study the key features of these equity-focused frameworks and models. We found 153 reviews focused on racial health equity, but only two of them used logic models to explain the intervention. From interviews and website searches, we identified seven existing health equity frameworks. These were grouped into four categories: research, health policy, digital health care, and clinical preventive services. Only two of these frameworks provided advice on how to use them in evidence reviews, while most focused on understanding how race and racism impact health as a social factor. In conclusion, there is limited use of frameworks and models in reviews about racial health equity. More practical frameworks are needed to help guide the research and interpretation of these reviews.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.413 | 0.683 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.018 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".