Criteria for selection to anesthesia residency programs: a survey of Canadian anesthesia program directors
Bibliographic record
Abstract
Introduction: Applicants to specialty programs lack guidance on knowing what exactly is desired by selection committees and program directors. Anesthesia is especially opaque, given its failure to provide transparency reports nationally. This study was developed to survey Canadian anesthesia program directors about the aspects of the application package desired in an anesthesia applicant. The primary objective is to identify the preferred attributes of anesthesia applications by those mandating the selection committees. Methods: Survey was developed via Google Surveys, and sent online over a period of two months in June and July 2020. All program directors were sent requests for filling in the survey. STATA was used for all statistical analyses. Two analyses, Mann-Whitney and ANOVA tests, were performed for comparison groups. A p < 0.05 was considered significant. Results: Fourteen of seventeen (83%) Canadian anesthesia program directors completed the survey. Having done an anesthesia elective, good performance in it, and excellence of preclinical academic performance were considered among the most important aspects of the application package with the highest ranking important and smallest standard deviation. Any form of red flag was also considered an important criterion, again with little variation among program directors. The reference letters selected by the applicants were also important, with a personal relationship and well written reference being identified as most important (p < 0.05). Conclusions: An applicant who has good academic performance, having anesthesia elective experience, personal, well-written reference letters, and general activity and interests that are not necessarily anesthesia-focused would be favoured by Canadian anesthesia programs.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".