Burnout among Residents: Prevalence and Predictors of Depersonalization, Emotional Exhaustion and Professional Unfulfillment among Resident Doctors in Canada
Bibliographic record
Abstract
BACKGROUND: Burnout in the medical profession has garnered a lot of attention over recent years. It has been reported across all specialties and all stages of medical education; however, resident doctors in particular are at risk for burnout throughout their years of training. This study was aimed at evaluating the prevalence and correlates of burnout among resident doctors in Alberta. METHODS: Through a descriptive cross-sectional study design, a self-administered questionnaire was used to gather data from resident doctors at two medical schools in Alberta, Canada. The Maslach Burnout Inventory was used as the assessment tool. Chi-squared and multivariate binary logistic regression analyses were used. RESULTS: Overall burnout prevalence among residents was 58.2%, and for professional fulfilment index, it was 56.7% for work exhaustion and interpersonal disengagement and 83.5% for lack of professional fulfillment. Working more than 80 h/week (OR = 16.437; 95% CI: 2.059-131.225), being dissatisfied (OR = 22.28; 95% CI: 1.75-283.278) or being neither satisfied nor dissatisfied with a career in medicine (OR = 23.81; 95% CI: 4.89-115.86) were significantly associated with high depersonalization. Dissatisfaction with efficiency and resources (OR = 10.83; CI: 1.66-70.32) or being neither satisfied nor dissatisfied with a career in medicine (OR = 5.14; CI: 1.33-19.94) were significantly associated with high emotional exhaustion. Working more than 80 h/week (OR = 5.36; CI: 1.08-26.42) and somewhat agreeing that the residency program has enough strategies aimed at resident well-being in place (OR = 3.70; CI: 1.10-12.46) were significantly associated factors with high work exhaustion and interpersonal disengagement. A young age of residents (≤30 years) (OR = 0.044; CI: 0.004-0.445) was significantly associated with low professional fulfillment. CONCLUSION: Burnout is a serious occupational phenomenon that can degenerate into other conditions or disrupt one's professional performance. Significant correlates were associated with high rates of burnout. Leaders of medical schools and policymakers need to acknowledge, design, and implement various strategies capable of providing continuous effective mental health support to improve the psychological health of medical residents across Canada.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".