Black Community Health Advocates in Ontario: A Look at Health Policy Engagement From the Ground Up
Bibliographic record
Abstract
Study objectives: Disproportionately negative pandemic outcomes, lack of race-based data collection and poor engagement of Black communities in policy decision making have been widely documented for Black Canadians. We examine this to understand how formal public engagement processes might be more inclusive of Black peoples to inform more responsive policies. Methodology: cases, including people who have been at the forefront of high-impact work in this space and (2) participants whose mission and mandates represented diverse approaches and sub-populations. Results: Our findings suggest that while Black community advocates face systemic and contextual barriers, they also embody deep and multifaceted knowledge, training and experience, which inform the rich ways that they approach advocacy. Discussion: Despite its Ontario focus, this study adds breadth and depth to the existing literature on health policy and historically marginalized populations, offering broader lessons for policy makers across jurisdictions. Our findings encourage policy makers to better recognize, make space for and cultivate fertile advocacy foundations, cultural knowledge and community-driven systems already present in Black communities.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.047 | 0.012 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".