Paper 5: a methodological overview of methods and interventions
Bibliographic record
Abstract
OBJECTIVES: We aim to (1) evaluate the methods used in systematic reviews of interventions focused on racialized populations to improve racial health equity and (2) examine the types of interventions evaluated for advancing racial health equity in systematic reviews. STUDY DESIGN AND SETTING: We searched MEDLINE, Cochrane, and Campbell databases for reviews evaluating interventions focused on racialized populations to mitigate racial health inequities, published from January 2020 to January 2023. RESULTS: We analyzed 157 reviews on racialized populations. Only 22 (14%) reviews addressed racism's role in driving racial health inequities related to the review question. Eleven percent (7) of reviews considered intersectionality when conceptualizing racial inequities. Two-thirds (105, 67%) provided descriptive summaries of included studies rather than synthesizing them. Among those that quantified effect sizes, 54% (21) used biased synthesis methods like vote counting. The most common method assessed was tailoring interventions to meet the needs of racialized populations. Reviews mainly focused on assessing interventions to reduce racial disparities rather than enhancing structural opportunities for racialized populations. CONCLUSION: Reviews for racial health equity could be improved by enhancing methodologic quality, defining the role of racism in the question, using reliable analytical methods, and assessing process and implementation outcomes. More focus is needed on assessing structural interventions to improve opportunities for racialized populations and prioritize these issues in political and social agendas.
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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 | 0.328 | 0.509 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".