Characteristics and practice patterns of international medical graduates: how different are they from those of Canadian-trained physicians?
Bibliographic record
Abstract
OBJECTIVE: To investigate the personal characteristics and practice patterns of international medical graduates (IMGs) practising in southwestern Ontario and to compare them with the personal characteristics and practice patterns of Canadian-trained family physicians practising in the same region. DESIGN: Cross-sectional analysis of data gathered from a census of family physicians. SETTING: Southwestern Ontario. PARTICIPANTS: A total of 685 family physicians. MAIN OUTCOME MEASURES: Characteristics and practice patterns of IMG physicians and Canadian-trained physicians. RESULTS: Among all family physicians practising in southwestern Ontario, 15.3% were IMGs. The IMGs were more likely than Canadian-trained medical graduates to be older and to have been in practice longer, and less likely to have completed a family medicine residency or to have been involved in undergraduate or postgraduate teaching. The IMGs were more likely to have practised longer in their current locations and to be in solo practice and accepting new patients, but were less likely to be providing maternity and newborn care. They were also more likely than Canadian-trained medical graduates were to be serving in small towns and rural and isolated communities. CONCLUSION: The personal and practice characteristics of IMG physicians vary somewhat from those of their Canadian-trained colleagues. Policy efforts aimed at increasing and integrating IMG family physicians into the work force need to recognize these differences. Further research is needed before our results can be generalized to physicians practising beyond southwestern Ontario.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".