Patient-Reported Primary Health Care Experiences: The Challenges Faced by Nepalese Immigrant Men in Canada
Bibliographic record
Abstract
Background: Despite the Canadian universal healthcare system, new immigrants face a number of challenges in accessing primary healthcare (PHC) services. As the level of immigration to Canada continues to grow, it is crucial to understand the nature of barriers to care immigrants face Methods: A qualitative research using focus groups was conducted among a sample of first-generation Nepalese immigrant men who had some experience with PHC in Canada. Data collection and analysis: A total of 6 focus groups were conducted among 34 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 to 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 waiting time for an appointment with specialists, limited knowledge of own health- and services-related issues, limitedservice 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 family doctors during the appointment, communication problems and misunderstandings, high healthcare costs associated with dental and vision care and prescribed medicines, and inappropriate behaviors and practices of doctors and service providers. Conclusions: Accessible primary healthcare is important for the health of immigrant populations in Canada. It is important to recognize the extent of barriers to effectively shape public policy and improve access to primary healthcare
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".