Burnout among public health physicians and residents in Canada following the COVID-19 pandemic: A cross-sectional study
Bibliographic record
Abstract
Previous research identified high levels of burnout in the Canadian public health workforce during the COVID-19 pandemic. This study presents the prevalence of burnout, associated participant demographic and workplace characteristics, and associated secondary outcomes among Canadian public health physicians and residents one year after the end of the COVID-19 pandemic. Data were collected using an online survey distributed through Canadian public health associations and professional networks between April and May 2024. Validated tools were used to measure burnout (Oldenburg Burnout Inventory (OLBI)), screen for anxiety (GAD-2) and depression (PHQ-2), and professional fulfillment (Stanford Professional Fulfillment Index). Additional binary (yes/no) questions were asked on workplace safety topics (e.g., threats, assaults, being bullied) and professional plans. Fisher's exact test and logistic regressions were used to model the association between burnout and sequelae of burnout, including symptoms of depression and anxiety, and professional fulfillment. Among 118 physicians who completed the OLBI, the prevalence of burnout was 63.6%. Additionally, 41.2% of physicians reported being threatened, assaulted or bullied during the pandemic. Physicians who screened positive for anxiety (19.3%) and depression (7.6%) had higher odds of burnout (OR 4.79, 95% CI 1.29-26.90, p = 0.01 and OR 2.10, 95% CI 0.38-21.65, p = 0.48, respectively). Moreover, physicians who had low levels of professional fulfillment (84.9%) also had higher odds of burnout (OR 12.5, 95% CI 3.21-72.76, p < 0.001). The prevalence of burnout among Canadian public health physicians and residents remains high post-pandemic and was associated with symptoms of depression, anxiety and low professional fulfillment. By implementing interventions to prevent and mitigate burnout, and promote recovery, the public health system will be better positioned to recruit and retain physicians to serve the population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".