HEIA tools: inclusion of migrants in health policy in Canada
Bibliographic record
Abstract
This paper introduces the Migrant Populations Equity Extension for Ontario's Health Equity Impact Assessment (HEIA) initiatives. It provides a mechanism to address the needs of migrant populations, within a program and policy framework. Validation of an equity extension framework using community leaders and health practitioners engaged in HEIA workshops across Ontario. Participants assessed migrants' health needs and discussed how to integrate these needs into health policy. The Migrant Populations Equity Extension's framework assists decision makers assess relevant populations, collaborate with immigrant communities, improve policy development and mitigate unintended negative impacts of policy initiatives. The tool framework aims to build stakeholder capacity and improve their ability to conduct HEIAs while including migrant populations. The workshops engaged participants in equity discussions, enhanced their knowledge of migrant policy development and promoted HEIA tools in health decision-making. Prior to these workshops, many participants were unaware of the HEIA tool. The workshops informed the validation of the equity extension and support materials for training staff in government and public health. Ongoing research on policy implementation would be valuable. Public health practitioners and migrant communities can use the equity extension's framework to support decision-making processes and address health inequities. This framework may improve policy development and reduce health inequities for Ontario's diverse migrant populations. Many countries are now using health impact assessment and health equity frameworks. This migration population equity extension is an internationally unique framework that engages migrant communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".