Cross-Sectional Analysis of Canadian Anesthesiology Residency Program Website Content
Bibliographic record
Abstract
Background Residency program websites are an important resource widely used by prospective applicants when applying to programs. The objectives of our study were to evaluate the program content available on Canadian anesthesiology residency program websites using established criteria and identify any areas for improvement. Methods In this cross-sectional study, we evaluated the content available on accredited anesthesiology residency training program websites, between July and August 2021, using 54 criteria provided in the following domains: recruitment; faculty; residents; education and research; clinical work; incentives; wellness; and environment. Website scores were analyzed using descriptive statistics and presented as median (interquartile range), percentage (%), and range. Results We identified 17 programs with publicly available functional websites. Overall, residency programs met a median of 28 (interquartile range: 18-36) website criteria out of 54 (51.9%). Education and research was the highest-scoring domain among residency programs (median 77.8% of criteria met), while resident information and incentives were the lowest (14.3%). Conclusion Canadian anesthesiology residency program websites include information on many domains relevant to prospective applicants, including education and research. However, most websites require improvement and content updates for faculty information, resident information, incentives, wellness, and environment.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".