Attracting top residency candidates: a survey of important program attributes.
Bibliographic record
Abstract
OBJECTIVES: Emergency medicine (EM) residents work intimately with emergency department staff, and many residents become staff at the institutions that train them. As such, it is in the interest of all training sites to attract the strongest candidates to their programs. The goal of this study was to determine what factors make programs most appealing to EM residency applicants. METHODS: A survey was developed to assess the relative importance of 20 factors used by EM residency applicants in selecting a Royal College of Physicians and Surgeons of Canada residency program. The survey was piloted on 17 University of Western Ontario EM residency candidates in 2003, and validated on 26 EM residency candidates applying to 8 sites across Canada in 2004. RESULTS: The 20 surveyed factors fell into 4 categories. The most important factors were those relating to interactions with the program (4.5 out of 5), followed by factors relating to the program itself (3.5), personal factors (3.4), and lastly, factors relating to the city/province (2.9). CONCLUSIONS: These data suggest that the most important factors are "interactions with a program" and program characteristics. Both of these are largely within a program's control. By striving to make their curriculum, interview days and medical student electives more appealing a residency program can improve its ability to attract the strongest residency candidates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".