Policies and Health Inequalities: State of the Field and Future Directions
Bibliographic record
Abstract
In contrast to inequalities in health that stem from biological differences brought about by age or genetics, social inequalities in health are mutable and avoidable as they are affected by public policies. In recognition of the importance of these social influences on population health and inequalities, the World Health Organization adopted, in 2012, resolution WHA62.14 endorsing the Rio Political Declaration on Social Determinants of Health. With this resolution, member states recognize the existence of social determinants of health (SDH) and pledge to implement actions outlined in the Rio declaration, including to “monitor progress and increase accountability to inform policies on SDH.” From 7 to 9 May 2014, the Quebec Inter-University Centre for Social Statistics held an international conference in Montreal. “Social Policy and Health Inequalities: An International Perspective” had as its primary objective showcasing leading-edge international research on the impact of social policies and programs on health inequalities in high-income countries. More specifically, the conference aimed to encourage international exchanges in order to demonstrate the range of research practices and outputs as well as to stimulate debate among the key stakeholders in this research process: organizations that produce statistics, researchers, and knowledge users.
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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 | 0.044 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.016 | 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".