Residents' satisfaction with Canadian otolaryngology-head and neck surgery programs.
Bibliographic record
Abstract
OBJECTIVE: To describe residents' satisfaction with Canadian Otolaryngology-Head and Neck Surgery programs. DESIGN: Electronic survey of 21 items scored on a 5-point Likert scale. SETTING: All Canadian Otolaryngology-Head and Neck Surgery residency programs were surveyed between February and April 2008. Responses were anonymous and on a voluntary basis. METHODS: All Canadian Otolaryngology-Head and Neck Surgery residents were surveyed between February and April 2008. Responses were anonymous and on a voluntary basis. Overall and selected item scores were compared between sexes with the t-test and between postgraduate year levels with analysis of variance. Linear regression model was used to identify factors that would predict burnout. MAIN OUTCOME MEASURES: Item scores and overall score. RESULTS: Ninety-two of 140 residents responded (66%), including 23 female residents. More than 80% of residents agreed or strongly agreed with the following statements: satisfied with surgical volume (90%), satisfied with the amount of active operative participation (82%), confident about their surgical skills (90%), satisfied with teaching from attending staff (84%), have role models for career (93%) and have a good working environment (89%). Weaker areas identified included feedback, research support, and balance in life. Self-perceived burnout prevalence was 33%, with statistically significant higher rates in English programs (p < .001). No difference in both overall and item score was identified between sexes. Ninety-one percent of residents would still choose Otolaryngology-Head and Neck Surgery as a specialty. CONCLUSION: Overall, Canadian Otolaryngology-Head and Neck Surgery residents are very satisfied despite the fact that one-third reported suffering from burnout.
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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) | 0.005 | 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".