Learning to practice in Canada: The hidden curriculum of international medical graduates
Bibliographic record
Abstract
INTRODUCTION: There is movement of physicians internationally. In some cases, physicians are recruited from low-income countries to wealthier countries like Canada to provide medical services in underresourced communities. This needs assessment examined the clinical medicine learning challenges faced by international medical graduates (IMGs) from the perspective of both the IMGs and medical leaders (eg, Vice President-Medical for a Health Region). METHODS: Focus groups with 25 IMGs were held in 6 regional centers. Face-to-face interviews were held with 10 medical leaders. Participants were asked about the learning associated with patient management, patient referral, and investigation, for billing and insurance, and learning about new systems of care. Qualitative data were analyzed to determine how well the perspectives on learning were aligned. RESULTS: IMGs and medical leaders recognized that learning and support were needed by physicians without previous experience in Canada. They had similar lists of learning issues. Although medical leaders believed the new information was explicit, readily available, and could be learned from short explanations and lists; IMGs found that guidelines and expectations were implicit, confusing, and contradictory. There were mediating influences in the form of orientation programs, other IMGs, and "how to" lists in some cases, which helped the newcomer. DISCUSSION: There was concordance about aspects of the learning that was required between IMGs and medical leaders. There was little agreement about the approach to learning or a recognition that the learning tasks were complicated.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".