Rejecting and accepting international migrant patients into primary care practices: a mixed method study
Bibliographic record
Abstract
Purpose – International migrants frequently struggle to obtain access to local primary care practices. The purpose of this paper is to explore factors associated with rejecting and accepting migrant patients into Canadian primary care practices. Design/methodology/approach – Mixed methods study. Using a modified Delphi consensus approach among a network of experts on migrant health, the authors identified and prioritized factors related to rejecting and accepting migrants into primary care practices. From ten semi-structured interviews with the less-migrant-care experienced practitioners, the authors used qualitative description to further examine nuances of these factors. Findings – Consensus was reached on practitioner-level factors associated with a reluctance of practitioners to accept migrants − communication challenges, high-hassle factor, limited availability of clinicians, fear of financial loss, lack of awareness of migrant groups, and limited migrant health knowledge – and on factors associated with accepting migrants − feeling useful, migrant health education, third party support, learning about other cultures, experience working overseas, and enjoying the challenge of treating diseases from around the world. Interviews supported use of interpreters, community resources, alternative payment methods, and migrant health education as strategies to overcome the identified challenges. Research limitations/implications – This Delphi network represented the views of practitioners who had substantive experience in providing care for migrants. Interviews with less-experienced practitioners were used to mitigate this bias. Originality/value – This study identifies the facilitators and challenges of migrants’ access to primary care from the perspective of primary care practitioners, work that complements research from patients’ perspectives. Strategies to address these findings are discussed.
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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 | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".