Advancing health equity to improve health: The time is now
Bibliographic record
Abstract
Health inequities, or avoidable inequalities in health between groups of people, are increasingly recognized and tackled to improve public health.Canada's interest in health inequities goes back over 40 years, with the landmark 1974 Lalonde report, and continues with the 2011 Rio Political Declaration on Social Determinants of Health, which affirmed a global political commitment to implementing a social determinants of health approach to reducing health inequities.Research in this area includes documenting and tracking health inequalities, exploring their multidimensional causes, and developing and evaluating ways to address them.Inequalities can be observed in who is vulnerable to infectious and chronic diseases, the impact of health promotion and disease prevention efforts, how disease progresses, and the outcomes of treatment.Many programs, policies and projects with potential impacts on health equity and determinants of health have been implemented across Canada.Recent theoretical and methodological advances in the areas of implementation science and population health intervention research have strengthened our capacity to develop effective interventions.With the launch of a new health equity series this month, the journals Canada Communicable Disease Report (CCDR) and Health Promotion and Chronic Disease Prevention in Canada: Research, Policy and Practice (HPCDP Journal) will continue to reflect and foster analysis of social determinants of health and focus on intervention studies that advance health equity.
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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 | 0.021 | 0.067 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.018 | 0.048 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".