Predictors of Well-Being in Resident Physicians: A Descriptive and Psychometric Study
Bibliographic record
Abstract
BACKGROUND: It is important to describe the characteristics of well-being in resident physicians to develop resident wellness initiatives in postgraduate medical education. OBJECTIVE: To characterize the predictors of well-being in resident physicians by assessing personal and work-related burnout, work dissatisfaction, nutritional needs while on call, and sleep needs while on call. METHODS: We set up an online survey in 2012 to collect data from current residents at the University of Calgary in Canada. The WHO-Five Well-Being Index, personal and work-related subscales of the Copenhagen Burnout Inventory, questions on work dissatisfaction, as well as sleep and nutrition management needs while on call, were used in the survey. Descriptive statistics, univariate analysis, and linear regression were applied to the data. RESULTS: The survey response rate was 45% (317 of 706) of eligible residents, with a mean age of 30.9 years (SD = 4.3). Fifty-three percent (168 of 317) of residents had a well-being score of 13 or less, indicating poor mental well-being. There were significant differences between men and women with respect to personal burnout (47.9 versus 54.2, P = .002) and work-related burnout (46.4 versus 50.4, P = .008). The only significant predictors of well-being overall were personal burnout and work dissatisfaction. CONCLUSIONS: Survey results suggest that a high proportion of residents at this institution have low well-being. This study did not find work-related burnout to be a significant predictor of well-being, after adjustment for other variables.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".