Job Satisfaction Among Plastic Surgery Residents in Canada: A National Survey
Bibliographic record
Abstract
Objective:Resident wellness is a focus of medical training and is prioritized in both Canadian and American accreditation processes. Job satisfaction is an important component of wellness that is not examined in the literature. The purpose of this study was to analyze job satisfaction in a national sample of plastic surgery residents, and identify factors that influence satisfaction.Methods:We designed a cross-sectional survey adapted from existing instruments, with attention to thorough item generation and reduction as well as pilot and clinical sensibility testing. All plastic surgery residents at Canadian institutions were surveyed regarding overall job satisfaction as well as personal- and program-specific factors that may affect satisfaction. Predictors of satisfaction were identified using multivariable regression models.Results:The response rate was 40%. Median overall job satisfaction was 4.0 on a 5-point Likert scale. Operative experience was considered both the most important element of a training program, and the area in most need of improvement. Senior training year (P < .01), shorter commute time (P = .04), fewer duty hours (P = .02), fewer residents (P < .01), and more fellows (P < .01) were associated with significantly greater job satisfaction.Conclusions:This is the first study to gather cross-sectional data on job satisfaction from a national sample of plastic surgery residents. The results from this study can inform programs in making tangible changes tailored to their trainees’ needs. Moreover, our findings may be used to inform a prospectively studied targeted intervention to increase job satisfaction and resident wellness to address North American accreditation standards.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".