Collaborative Priority Setting for Enhancing Primary Health Care Access among the Nepalese Community in Canada
Bibliographic record
Abstract
Background Extensive research concerning potential resolutions to immigrants' healthcare access in Canada is limited, and the viewpoint of immigrant communities regarding priorities and feasible solutions remains inadequately captured. The objective of this article is to portray a research endeavor in which grassroots community members assumed the role of priority-setters for research on primary care access concerns. Aim: This cross-sectional study aims to solicit input from Nepalese-Canadian immigrants in Calgary to rank ten predefined primary care access topics based on their perceived importance for research centered on solutions. Methods: A self-administered survey was conducted where ranking options for the ten primary care access challenge themes were provided to the participants. The themes were identified based on comprehensive literature reviews conducted by the members of the program of research. The survey questionnaire was pilot-tested and refined by team members before administering it. Results: We received 401 responses; of the respondents, 50.37% were men. There were significant differences between males and females in age, educational attainment, yearly household income, and length of stay in Canada variables. Healthcare costs, lack of resources, workplace-related barriers, cultural differences/preferences/perceptions, and transportation barriers were among the top-ranked research priorities by the participants. Conclusion: There is a growing recognition that health solution priority-setting approaches should embrace interdisciplinarity and collaboration, with community participation as a pivotal factor. This involvement enhances the healthcare system and fosters the creation of interventions that more effectively cater to the community's needs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".