Patient-reported primary health care experiences in Canada: The challenges faced by Nepalese immigrant men
Bibliographic record
Abstract
Background: Despite the Canadian universal healthcare system, new immigrants face a number of challenges in accessing primary healthcare (PHC) services. As immigration to Canada consistently increases, understanding various types of barriers to PHC and how they differ across different sub-groups is critical. We conducted a qualitative study among Nepalese immigrant men to learn from their experience with PHC access to inform healthcare providers, stakeholders, and policymakers to devise feasible approaches to enhancing access to care. Methods: We undertook a qualitative research approach employing focus groups among a sample of first-generation Nepalese immigrant men who had prior experience with accessing PHC in Canada. Data collection and analysis: We conducted six focus groups in total with 34 participants (each group comprising 5-7 participants) in their preferred language, Nepalese, or English. Demographic information was collected prior to each focus group. Transcriptions of the discussions were prepared, and thematic analysis was employed in the qualitative data set. Results: Participants reported experiencing barriers at two stages: before accessing PHC services and after accessing PHC services. The barriers before accessing PHC were long wait time for an appointment with physicians, limited knowledge of own health- and services-related issues, limited service availability hours, cultural differences in health practices, and transportation and work-related challenges. The barriers after accessing PHC were long wait time in the clinic to meet with the physicians at the time of appointment, communication challenges and misunderstandings, high healthcare costs associated with dental and vision care and prescribed medicines, and inappropriate behaviours and practices of doctors and service providers. To our knowledge, this is the first study in Canada which explored barriers faced by Nepalese immigrant men in accessing PHC. Conclusions: This study identifies barriers to accessing PHC in Canada from a group of immigrant men's perspective. It is important to account for these while making any reforms and adding new care services to the existing healthcare system so that they are equitable for these groups of individuals as well.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".