"We regret to inform you that you did not match": Reflections on how to improve the match experience
Bibliographic record
Abstract
Background: With the increasing awareness and action amongst stakeholders in addressing the concerning rise of unmatched Canadian Medical Graduates (CMGs), little is known from those who go unmatched. We use our unmatched experience to contribute to this dialogue. Methods: We present an issues-based examination of the matching process by reflecting on the pre- and post-match period, providing suggestions related to the Canadian context from the unmatched perspective. Results: The challenge in the pre-match period was handling uncertainty in elective scheduling. This uncertainty was largely manifested from not knowing elective availability at the time of elective application submission, as well as not knowing what “strategy” we should follow in how to structure our elective schedule. For the post-matched period, we were challenged by making decisions during a time-sensitive period, deciding on career issues like scheduling post-match electives, handling our finances, and trying to improve our future residency applications without feedback. Conclusion: Providing a real-time document of elective availability, providing focused feedback from our residency applications, and implementing and expanding upon extended curriculums for all medical schools to continue CMG training for their unmatched students for upcoming match cycles would greatly improve the unmatched experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".