Resident Depression and Burnout During the COVID-19 Pandemic: A Survey of Canadian Laboratory Medicine Trainees
Bibliographic record
Abstract
CONTEXT.—: Resident physicians face a higher rate of burnout and depression than the general population. Few studies have examined burnout and depression in Canadian laboratory medicine residents, and none during the COVID-19 pandemic. OBJECTIVE.—: To identify the prevalence of burnout and depression, contributing factors, and the impact of COVID-19 in this population. DESIGN.—: An electronic survey was distributed to Canadian laboratory medicine residents. Burnout was assessed using the Oldenburg Burnout Inventory. Depression was assessed using the Patient Health Questionnaire 9. RESULTS.—: Seventy-nine responses were collected. The prevalence of burnout was 63% (50 of 79). The prevalence of depression was 47% (37 of 79). Modifiable factors significantly associated with burnout included career dissatisfaction, below average academic performance, lack of time off for illness, stress related to finances, lack of a peer or staff physician mentor, and a high level of fatigue. Modifiable factors significantly associated with depression further included a lack of access to wellness resources, lack of time off for leisure, and fewer hours of sleep. Fifty-five percent (41 of 74) of participants reported direct impacts to their personal circumstances by the COVID-19 pandemic. CONCLUSIONS.—: Burnout and depression are significant issues affecting Canadian laboratory medicine residents. As the COVID-19 pandemic continues, we recommend the institution of flexible work arrangements, protected time off for illness and leisure, ongoing evaluation of career satisfaction, formal and informal wellness programming with trainee input, formal mentorship programming, and a financial literacy curriculum as measures to improve trainee wellness.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".