Community organizing for Black health equity in Canada: a multiple-case study analysis
Bibliographic record
Abstract
Context: Community organizing often plays a key aspect in voicing health inequities, improving health care access, implementing health promotion campaigns, or responding to public health crises. There is a paucity of research on community organizing for Black health in Canada. Objective: To examine how Black communities in Canada have historically organized to address systemic racism, advance health equity and/or respond to a public health crisis. To use multiple case study design to identify factors that contributed to success and long-term sustainability of community organizing for Black health. Study Design and Analysis: We searched the academic and grey literature from 1900 to present and included cases of community organizing and partnerships that were based in Canada, led by the Black community, and were an organized effort to improve health. From our pool of cases, we used purposive sampling and engaged community members to analyze three successful Black community organizing efforts for health: Women’s Health in Women’s Hands (local), the Health Association of African Canadians (provincial), and the Black Health Alliance (national). Setting and Population: Black communities in Canada. Conceptual Model: This study draws heavily from conceptual frameworks of Afrocentricity, Communitybased Participatory Research and Community Coalition Action Theory. Results: Our literature search identified eight cases of Black community organizing. Cases varied in terms of chronology, contextual and process factors, experience delivering primary care and sustainability. The multiple case study analysis shed light on several key similarities in how Black community organizing operationalised and used the frameworks to advance Black health equity. Conclusions: Community-oriented primary care can promote Black health equity by engaging Black community organizers and health care providers, advancing academic-community partnerships, and advocating for policy changes to address structural racism. Black community members draw on Afrocentric values of collective input, resistance, and strength to combat injustice and address health disparities. Using partnership principles and practices, these initiatives honor Black community knowledge and leadership, intersectionality, capacity-building, health literacy, and community transformation while seeking shared power to 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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".