Translating Research Evidence Into Practice to Reduce Health Disparities: A Social Determinants Approach
Bibliographic record
Abstract
Translating research evidence to reduce health disparities has emerged as a global priority. The 2008 World Health Organization Commission on Social Determinants of Health recently urged that gaps in health attributable to political, social, and economic factors should be closed in a generation. Achieving this goal requires a social determinants approach to create public health systems that translate efficacy documented by research into effectiveness in the community. We review the scope, definitions, and framing of health disparities and explore local, national, and global programs that address specific health disparities. Such efforts translate research evidence into real-world settings and harness collaborative social action for broad-scale, sustainable change.
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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 | 0.278 | 0.299 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.029 | 0.010 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.008 | 0.022 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".