International medical graduates: Learning for practice in Alberta, Canada
Bibliographic record
Abstract
INTRODUCTION: There is little known about the learning that is undertaken by physicians who graduate from a World Health Organization-listed medical school outside Canada and who migrate to Canada to practice. What do physicians learn and what resources do they access in adapting to practice in Alberta, a province of Canada? METHODS: Telephone interviews with a theoretical sample of 19 IMG physicians were analyzed using a grounded theory constant comparative approach to develop categories, central themes, and a descriptive model. RESULTS: The physicians described two types of learning: learning associated with studying for Canadian examinations required to remain and practice in the province and learning that was required to succeed at clinical work in a new setting. This second type of learning included regulations and systems, patient expectations, new disease profiles, new medications, new diagnostic procedures, and managing the referral process. The physicians "settled" into their new setting with the help of colleagues; the Internet, personal digital assistants (PDAs), and computers; reading; and continuing medical education programs. Patients both stimulated learning and were a resource for learning. DISCUSSION: Settling into Alberta, Canada, physicians accommodated and adjusted to their settings with learning activities related to the clinical problems and situations that presented themselves. Collegial support in host communities appeared to be a critical dimension in how well physicians adjusted. The results suggest that mentoring programs may be a way of facilitating settlement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".