An Evaluation of the Content of Canadian Plastic Surgery Residency Websites
Bibliographic record
Abstract
Background: Plastic surgery residency program websites are an important source of information to prospective applicants, especially given the ongoing COVID-19 pandemic and resulting suspension of all visiting electives and in-person interviews. This study aimed to analyze the online content of Canadian plastic surgery residency program websites. Methods: The content of all accredited Canadian plastic surgery residency websites was evaluated using 77-point criteria in the following 10 domains: recruitment, faculty, residents, research and education, surgical program, clinical work, benefits and career planning, wellness, environment, and gender of faculty leadership. Results: All accredited Canadian plastic surgery residency programs (n = 13) were identified using Canadian Resident Matching Service and had their dedicated program websites available for analysis. On average, residency program websites obtained a score of 33.5 (standard deviation = 13.7). The majority of programs did not score differently on the criteria by geographical distribution ( P > .05) nor by ranking ( P > .05). Conclusions: Most Canadian plastic surgery residency program websites are lacking content relevant to prospective applicants. Addressing inadequacies in online content may support programs to inform and recruit strong applicants into residency 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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".