Enhancing capacity of the coalition of African, Caribbean, and Black nurses to support Black nurses in British Columbia to achieve greater psychological health in the workplace
Bibliographic record
Abstract
This thesis, “Enhancing Capacity of the Coalition of African, Caribbean, and Black Nurses (CACBN) to Support Black Nurses in British Columbia to Achieve Greater Psychological Health in the Workplace” utilizes the methodological frameworks of the Action Research Engagement model, Black Feminist Thought and Intersectionality, Participatory Action Research and Appreciative Inquiry to answer the following question: How can the CACBN support Black nurses to achieve greater psychological health in the workplace? The African, Caribbean, and Black nurse participants were predominantly from CACBNs membership, held various nursing designations, and came from different practice environments. Data collection methods included a survey, interviews, and reflective journaling. Several sub-themes emerged from five overarching themes: Relational Connection: “Fitting In”, Factors that Contribute to Safety in the Workplace, System Level Supports Needed from Health Care Organizations, CACBN Supports for ACBNs, and Impacts of COVID-19 on ACBNs. Study recommendations for health organizations include developing workplace anti-racism policies and providing career supports and leadership opportunities, while recommendations for CACBN include providing anti-Black racism education to health authorities and schools, offering mentorship, and creating safe spaces for dialogue.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".