Examination outcomes for international medical graduates pursuing or completing family medicine residency training in Quebec.
Bibliographic record
Abstract
OBJECTIVE: To review the success of international medical graduates (IMGs) who are pursuing or have completed a Quebec residency training program and examinations. DESIGN: We retrospectively reviewed IMGs' success rates on the pre-residency Collège des médecins du Québec medical clinical sciences written examination and objective structured clinical examination, as well as on the post-residency Certification Examination in Family Medicine. SETTING: Quebec. PARTICIPANTS: All IMGs taking their examinations between 2001 and 2008, inclusive, and Canadian and American graduates taking their examinations during this same period. MAIN OUTCOME MEASURES: Success rates for IMGs on the pre-residency and post-residency examinations, compared with success rates for Canadian and American graduates. RESULTS: Success rates on the pre-residency clinical examinations remained below 50% from 2001 to 2008 for IMGs. Similarly, during the same period, the average success rate on the Certification examination was 56.0% for IMGs, compared with 93.5% for Canadian and American medical graduates. CONCLUSION: Despite pre-residency competency screening and in-program orientation and supports, a substantial number of IMGs in Quebec are not passing their Certification examinations. Another study is under way to analyze reasons for some IMGs' lack of success and to find ways to help IMGs complete residency training successfully and pass the Certification examination.
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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".