The acculturation of international medical graduates to Canadian practice
Bibliographic record
Abstract
This study explores the narratives of acculturation of ten international medical graduates (IMGs) from four different countries of origin who adapted to living and working in Canada. IMGs are a particularly interesting case of acculturation because medicine is a profession that demands a high degree of cultural competence and because it has a proscribed period of orientation in the form of post-graduate training or residency. This study asks the question how IMGs learn the implicit aspects of clinical practice. The semi-structured interviews of the IMGS were informed by acculturation psychology and transformative learning theory and guided by a model of cross-cultural learning suggested by Taylor (1994). Questions focused on how IMGs responded to cultural disequilibrium, what learning strategies they employed and how their identity changed. The conversations were recorded, transcribed and analyzed using a form of narrative inquiry. The narratives that emerged from the interviews suggest that cultural disequilibrium was diffuse and recursive, that the learning strategies were non-reflective and nondiscursive, and that individual identities evolved in a manner that suggests the archetypal hero's journey. The implications of these findings for the training of IMGs are explored and recommendations are made.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| 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".