Community-Driven Prioritization of Primary Health Care Access Issues by Bangladeshi-Canadians to Guide Program of Research and Practice
Bibliographic record
Abstract
Research around probable solutions to immigrants accessing health care in Canada is not extensive, and the perspective of immigrant communities on priorities and potential solutions has not been captured effectively. The purpose of this article is to describe a research initiative that involved grassroots community members as producers of research priorities on primary care access issues. This study aimed to seek input from an immigrant community in Calgary, Canada. Members of the Bangladeshi community of Calgary were asked through a survey to rank 10 predefined primary care access topics as to what they felt constituted priorities for solution-oriented research (1, highest; 10, lowest). We used frequencies and percentages to describe the participant demographics. Ratings of preferred research themes were analyzed on the basis of relative weighted priority rank. We received 432 responses: 51.2% female; 58.9% aged 36 to 55 years; 90.5% had university-level education; 46.2% immigrated to Canada between 10 and 19 years ago; 82.5% employed full/part-time or self-employed. Lack of resources, lack of knowledge, health care cost, and workplace-related barriers were among the top-ranked topics identified as solution-oriented research priorities. Through partnerships and reciprocal learning, public input can increase insider perspectives to help develop interventions that align with the needs of community members.
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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 | 0.026 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".