Understanding Research Participation Experiences Among Persons Identifying as African, Caribbean, and Black in British Columbia
Bibliographic record
Abstract
BACKGROUND: Health research in Canada has insufficiently engaged African, Caribbean, and Black (ACB) people, yielding under-representation of their priorities and unmet health needs. OBJECTIVE: To understand research experiences and priorities among ACB people in British Columbia. METHODS: Cross-sectional data from an online survey was summarized using descriptive statistics. Content analysis was used for open-ended text responses. Data collection and analysis was conducted with ACB community partners. RESULTS: Of 56 respondents, 50.0% were aged 16 to 25 years; 78.6% identified as women. Although only 42.9% had previous research experience, 91.1% were willing to participate. Participation barriers included time constraints (53.1%) and mistrust (30.6%). Facilitators included perceived benefits to ACB individuals/communities (83.9%) and opportunities to share perspective(s) (60.7%). Research priorities included mental health, substance use, and sexual and reproductive health. CONCLUSION: Findings highlight ACB individuals' willingness to participate in health research, while identifying participation barriers and facilitators. Researchers must build trust through anti-racist community engagement efforts.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".