Reducing social inequities in health through settings-related interventions — a conceptual framework
Bibliographic record
Abstract
INTRODUCTION: The creation of supportive environments for health is a basic action principle of health promotion, and equity is a core value. A settings approach offers an opportunity to bridge these two, with its focus on the interplay between individual, environmental and social determinants of health. METHODS: We conducted a scoping review of the literature on theoretical bases and practical applications of the settings approach. Interventions targeting social inequities in health through action on various settings were analyzed to establish what is done in health equity research and action as it relates to settings. RESULTS: Four elements emerged as central to an equity-focused settings approach: a focus on social determinants of health, addressing the needs of marginalized groups, effecting change in a setting's structure, and involving stakeholders. Each came with related challenges. To offer potential solutions to these challenges we developed a conceptual framework that integrates theoretical and methodological approaches, along with six core guiding principles, into a 'settings praxis'. CONCLUSIONS: Reducing social inequities in health through the creation of supportive environments requires the application of the settings approach in an innovative way. The proposed conceptual framework can serve as a guide to do so, and help develop, implement and evaluate equity-focused settings-related interventions.
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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 | 0.022 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".