Actions needed to promote health equity and the mental health of Canada’s Black refugees
Bibliographic record
Abstract
Objectives The overall goal was to synthesize knowledge on actions that need to be taken to promote health equity and the mental health of Black refugees in Canada.Design Group concept mapping systems were applied to generate and organize action-oriented statements related to the different social determinants of health. A total of 174 participants from the cities of Calgary and Edmonton with experience working with Black Canadians participated in four focus groups: (a) 2 focus groups that engaged 123 participants in brainstorming 84 statements guided by the following focus prompt: ‘A specific action that would improve the mental health equity of Black refugees living in Canada is … ’ and (b) 2 focus groups of 51 participants who sorted the generated statements and rated them by order of ‘importance’ and ‘ideas seen in action.’ Data was further computed and analysed by the research team and a select advisory group from the participants.Results A 10-cluster map generated included the following clusters: (1) promoting cultural identity, (2) promoting ways of knowing, (3) addressing discrimination and racism, (4) addressing the criminalization of Black Canadians, (5) investing in employment for equity, (6) promoting equity in housing, (7) facilitating self-determination, (8) improving (public) services, (9) promoting appropriate and culturally relevant mental health services, and (10) working with and addressing faith and belief related issues. Clusters 4 and 9 ranked as the most important clusters in promoting health equity and the mental health of Black Canadians.Conclusions Addressing the criminalization of Black Canadians through a range of rehumanizing interventions at institutional levels will provide a platform from which they can participate and engage others in developing appropriate and culturally relevant mental health services.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".