Self-study tool for integrating health equity into Health in All Policies (HiAP) initiatives
Bibliographic record
Abstract
SETTING: Setting. Health system leaders in Canada recognise that quality improvement alone cannot address health inequities. Intersectoral action, which involves coordination and collaboration across public, private, and third-sector organisations, can improve the distribution of social determinants of health (SDOH) and thereby, health equity. While Health in All Policies (HiAP) promotes this approach, critiques and empirical data highlight implementation gaps over whether health equity is actually being improved. The potential for HiAP initiatives to reduce health inequities can be strengthened by paying greater attention to how these interventions are designed, implemented, and evaluated. INTERVENTION: We developed and pilot tested a self-study tool that helps organisations learn and reflect on how health equity can be targeted in intersectoral initiatives, including HiAP. This is not the only tool that can be used to consider ways to integrate health equity into intersectoral action; however, it is the first one designed for HiAP initiatives specifically. OUTCOMES: The self-study tool asks the user to reflect on a series of health equity concepts to raise awareness about opportunities to better integrate health equity into the design, implementation, and evaluation of intersectoral initiatives. IMPLICATIONS: The survey and appendix can fill in the gaps of other tools meant to support intersectoral action for health by focusing on ways to strengthen the health equity potential of initiatives. Users can apply the tool prospectively and retrospectively to explicitly target specific criteria to improve how their interventions focus on and potentially address 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 | 0.048 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 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".