Research with Black Communities to Inform Co-Development of a Framework for Anti-Racist Health and Community Programming
Bibliographic record
Abstract
Study BackgroundThe effects of systemic racism were exacerbated and amplified throughout the COVID-19 pandemic. The resurgence of the "Black Lives Matter" movement in North America brought awareness to the public, especially white people, of the impacts of systemic racism in society and the urgent need for large-scale and institutional anti-racism work.PurposeIn collaboration with a local Community Health Centre, this research focused on identifying priority areas for tailored and co-developed anti-Black racism interventions in health services and community programming, as well as examining how purposeful relationships can be created with African, Caribbean, and Black (ACB) communities in London, Ontario.MethodsSemi-structured interviews were conducted in either French or English with nine formal or informal leaders from London's ACB communities. Interpretive description methodology guided analysis and interpretation.ResultsParticipants indicated that anti-Black racism is ever-present in the community and in their lives, with systemic racism causing the most harm. Racism should be addressed by creating ACB-specific services, and education for non-Black communities; increased representation, inclusion, and engagement of ACB people within organizations, especially in leadership roles, are essential. A framework based on study findings to guide how organizations can develop authentic and purposeful relationships with ACB communities is presented.ConclusionsOrganizations will continue to perpetuate systemic racism unless they actively seek to be anti-racist and implement strategies and policies to this end. The proposed framework can guide partnerships between health and community organizations and ACB communities, and support co-development of strategies to address anti-Black racism.
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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 | 0.072 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.035 | 0.043 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".