Evidence review: Settings for addressing the social determinants of health inequities
Bibliographic record
Abstract
This report provides an overview of the evidence base on work in health promotion settings that addresses the social determinants of health inequities. The review identifies key aspects of ‘what works’ to reduce health inequities in settings through focussing on social determinants of health. It also provides recommendations for future planning, action and research. We note that while we identified much health promotion activity in settings, only a fraction of this addresses one or more social determinants of health. Furthermore, even where settings-based approaches are addressing social determinants, most work reports only on population outcomes and there is a distinct lack of studies which explicitly evaluate the impact on health equity. Making the everyday settings of people’s lives – where they live, love, play, work and google – more supportive of healthy choices has long been recognised by health promoters as an optimum way to improve population health. The World Health Organisation's Ottawa Charter (1986) recognises that health is created and lived by people within these settings and that policies and institutional practices shape the opportunities people have to lead healthy lives and make healthy choices. Addressing social determinants within settings is particularly relevant following three major reports which identify this as the most significant way to improve health equity. These are Closing The Gap in A Generation: Health Equity Through Action on the Social Determinants of Health (Commission on the Social Determinants of Health, CSDH 2008); Fair Society, Healthy Lives (The Marmot Review): Strategic Review of Health Inequalities in England Post 2010 (Marmot et al, 2010) and the WHO European Review of Social Determinants of Health & the Health Divide (Marmot et al 2012).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 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".