Monitoring health inequalities to inform action- the Pan-Canadian Health Inequalities Reporting Initiative
Bibliographic record
Abstract
Abstract Improving national health inequalities monitoring and reporting systems is critical for informing effective action to improve health equity. The Pan-Canadian Health Inequalities Reporting Initiative (HIRI) provides a foundation of data and evidence to support collaborative efforts to reduce health inequalities in Canada. HIRI is led by the Public Health Agency of Canada, in collaboration with the Pan-Canadian Public Health Network, Statistics Canada, First Nation Information Governance Centre and other partners. HIRI brings together inequity measures for more than 100 indicators of health outcomes, risk factors, and social determinants of health disaggregated across a range of socioeconomic and sociodemographic variables meaningful to health equity including: sex/gender; age; income; education; employment; occupation; immigrant status; Indigenous identity; race/ethnicity; urban/rural residence; material and social deprivation, functional health/participation and activity limitation and sexual orientation. HIRI aims to strengthen health inequalities measurement, monitoring, and reporting capacity in Canada. It informs policy and program decision making to more effectively reduce health inequalities, and enables the monitoring of progress in this area. This presentation will provide an overview of the HIRI along with the key health inequalities in Canada. It will elaborate on the importance of the engagement with multiple partners in creating a broad range of data and knowledge translation products, and discuss how making it accessible to others: Allows for a comprehensive and systematic assessment of the state of health inequalities in CanadaSupports focused action through increased access to health inequalities knowledgePromotes collaboration and accountability for the reduction of health inequalities in order to achieve SDGs
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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 | 0.114 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".