Paper 2: themes from semistructured interviews
Bibliographic record
Abstract
OBJECTIVES: In the context of profound and persistent racial health inequities, we sought to understand how to define racial health equity in the context of systematic reviews and how to staff, conduct, disseminate, sustain, and evaluate systematic reviews that address racial health equity. STUDY DESIGN AND SETTING: The study consisted of virtual, semistructured interviews followed by structured coding and qualitative analyses using NVivo. RESULTS: Twenty-nine individuals, primarily United States-based, including patients, community representatives, systematic reviewers, clinicians, guideline developers, primary researchers, and funders, participated in this study. These interest holders brought up systems of power, injustice, social determinants of health, and intersectionality when conceptualizing racial health equity. They also emphasized including community members with lived experience in review teams. They suggested making changes to systematic review scope, methods, and eligible evidence (such as adapting review methods to include racial health equity considerations in prioritizing topics for reviews, formulating key questions and searches, and specifying outcomes) and broadening evidence to include designs that address implementation and access. Interest holders noted that sustained efforts to center racial health equity in systematic reviews require resources, time, training, and demonstrating value to funders. CONCLUSION: Interest holders identified changes to the funding, staffing, conduct, dissemination, and implementation of systematic reviews to center racial health equity. Action on these steps requires clear standards for success, an evidence base to support transformative changes, and consensus among interest holders on the way forward.
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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 | 0.037 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".