How do IMGs compare with Canadian medical school graduates in a family practice residency program?
Bibliographic record
Abstract
OBJECTIVE: To compare international medical graduates (IMGs) with Canadian medical school graduates in a family practice residency program. DESIGN: Analysis of the results of the in-training evaluation reports (ITERs) and the Certification in Family Medicine (CCFP) examination results for 2 cohorts of IMGs and Canadian-trained graduates between the years 2006 and 2008. SETTING: St Paul's Hospital (SPH) in Vancouver, BC, a training site of the University of British Columbia (UBC) Family Practice Residency Program. PARTICIPANTS: In-training evaluation reports were examined for 12 first-year and 9 second-year Canadian-trained residents at the SPH site, and 12 first-year and 12 second-year IMG residents at the IMG site at SPH; CCFP examination results were reviewed for all UBC family practice residents who took the May 2008 examination and disclosed their results. MAIN OUTCOME MEASURES: Pass or fail rates on the CCFP examination; proportions of evaluations in each group of residents given each of the following designations: exceeds expectations, meets expectations, or needs improvement. The May 2008 CCFP examination results were reviewed. RESULTS: Compared with the second-year IMGs, the second-year SPH Canadian-trained residents had a greater proportion of exceeds expectations designations than the IMGs. For the first-year residents, both the SPH Canadian graduates and IMGs had similar results in all 3 categories. Combining the results of the 2 cohorts, the Canadian-trained residents had 310 (99%) ITERs that were designated as either exceeds expectations or meets expectations, and only 3 (1%) ITERs were in the needs improvement category. The IMG results were 362 (97.6%) ITERs in the exceeds expectations or meets expectations categories; 9 (2%) were in the needs improvement category. Statistically these are not significant differences. Seven of the 12 (58%) IMG candidates passed the CCFP examination compared with 59 of 62 (95%) of the UBC family practice residents. CONCLUSION: The IMG residents compared favourably with their Canadian-trained colleagues when comparing ITERs but not in passing the CCFP examination. Further research is needed to elucidate these results.
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".