The Relative Influence of Available Resources During the Residency Match: A National Survey of Canadian Medical Students
Bibliographic record
Abstract
BACKGROUND: Many medical students find choosing a residency challenging. There are several steps involved, including determining one's own priorities, arranging electives, choosing a training program and site, and preparing an in-depth application and a rank order list. Many resources are available to assist students, including the Canadian Resident Matching Service website, program websites, career counselors, career information sessions, mentors, peers, family/friends, and the Canadian Medical Residency Guide. Our study explored the relative impact of these resources on the career decision-making process. METHOD: We invited medical students in their final year at 12 Canadian medical schools to complete an online survey. Questions included identifying the relative utility of resources in the context of each component of the decision-making process. Responses were analyzed using descriptive statistics. RESULTS: The response rate was 71% (1076 of 1518). Overall, mentors, family/friends, and peers had the most impact on students' decision making. Career counselors, websites, and the Canadian Medical Residency Guide had much less impact. Family/friends were most frequently cited as essential to the process; however, family/friends and peers were equal in having some impact. CONCLUSIONS: Our findings suggest that students are most influenced by family, friends, and peers, who are not involved in the formal residency selection effort. Appreciating the impact of these influences on students' decision making is important to understanding how they decide on their future careers. The study supports continuation of mentorship programs. Future work could focus on qualitative research to further characterize resource use.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 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".