International medical graduates’ experiences with caring for cross-cultural patient populations
Bibliographic record
Abstract
OBJECTIVE: To explore experiences of international medical graduate (IMG) FPs in providing cross-cultural patient care and to identify rewards and challenges they experienced when caring for patients of cultural backgrounds different from their own. DESIGN: Descriptive qualitative study. SETTING: Family medicine primary care practices in Alberta. PARTICIPANTS: Eighteen IMG FPs practising in the metropolitan areas of Edmonton or Calgary in Alberta as of May 2013. METHODS: Individual face-to-face or telephone interviews were conducted using a semistructured interview guide. Seventeen interviews occurred between July and August 2013 and 1 took place in August 2014. All interviews were audiorecorded and transcribed verbatim. Transcribed data were subject to thematic analysis. MAIN FINDINGS: International medical graduates identified several rewarding aspects of caring for patients with cultural backgrounds different from their own, including learning about different cultures, perceiving that appointments are more succinct, and advocating for patients whom they perceive to be at a disadvantage. Family physicians also identified several challenges associated with caring for patients of different cultural backgrounds, including encountering language barriers, perceiving that visits take longer, and experiencing patients' lack of acceptance of FPs with cultural backgrounds different from their own. CONCLUSION: Cultural differences between FPs and patients can enhance or undermine doctor-patient relationships. The results of this study speak to the need for cultural competency training for FPs practising in culturally diverse settings.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".