Barriers to primary care among immigrants and refugees in Peterborough, Ontario: a qualitative study of provider perspectives
Bibliographic record
Abstract
BACKGROUND: Canada's immigrants and refugees have often settled in large Canadian cities, but this is changing with rising costs of living and rural settlement initiatives. However, little consideration is made regarding systemic changes needed to accommodate this distribution, particularly in healthcare in medium-sized cities or smaller communities. For most Canadians, primary care is an entry point into the healthcare system but immigrants and refugees face unique barriers to accessing care compared to the general Canadian population. This project aimed to better understand the barriers to accessing primary care among newcomers in Peterborough, Ontario from the perspective of newcomer service providers. METHODOLOGY: Participants were recruited from community organizations identified by the local settlement agency, the New Canadians Centre, as having regular interactions with newcomer clients including clinics, not-for-profit organizations, and volunteer groups. Four focus groups were completed, each with three participants (n=12). A coding grid was deductively developed to guide thematic analysis by adapting Levesque et al.'s conceptual framework defining access to healthcare with five specific dimensions: approachability, acceptability, availability and accommodation, affordability, and appropriateness. RESULTS: Participants identified lack of awareness of the healthcare system, stigma, competing priorities, and direct costs as some of the barriers for newcomers. Participants highlighted barriers unique to Peterborough including proximity to services, social isolation, and a shortage of family physicians. The results also highlighted strengths in the community such as its maternal-child health programming. CONCLUSION: The results provide a glimpse of the challenges to accessing primary care among newcomers in medium-sized communities and identify opportunities to prepare for changing settlement patterns.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 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".