Understanding the mental health and intention to leave of the public health workforce in Canada during the COVID-19 pandemic: A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: There is limited evidence about the mental health and intention to leave of the public health workforce in Canada during the COVID-19 pandemic. The objectives of this study were to determine the prevalence of burnout, symptoms of anxiety and depression, and intention to leave among the Canadian public health workforce, and associations with individual and workplace factors. METHODS: A cross-sectional study was conducted using data collected by a Canada-wide survey from November 2022 to January 2023, where participants reported sociodemographic and workplace factors. Mental health outcomes were measured using validated tools including the Oldenburg Burnout Inventory, the 7-item Generalized Anxiety Disorder scale, and the 2-item Patient Health Questionnaire to measure symptoms of depression. Participants were asked to report if they intended to leave their position in public health. Logistic regression was used to estimate adjusted odds ratios (aOR) and 95% confidence intervals (95% CI) for the associations between explanatory variables such as sociodemographic, workplace factors, and outcomes of mental health, and intention to leave public health. RESULTS: Among the 671 participants, the prevalence of burnout, and symptoms of depression and anxiety in the two weeks prior were 64%, 26%, and 22% respectively. 33% of participants reported they were intending to leave their public health position in the coming year. Across all outcomes, sociodemographic factors were largely not associated with mental health and intention to leave. However, an exception to this was that those with 16-20 years of work experience had higher odds of burnout (aOR = 2.16; 95% CI = 1.12-4.18) compared to those with ≤ 5 years of work experience. Many workplace factors were associated with mental health outcomes and intention to leave public health. Those who felt bullied, threatened, or harassed because of work had increased odds of depressive symptoms (aOR = 1.85; 95% CI = 1.28-2.68), burnout (aOR = 1.61; 95% CI = 1.16-2.23), and intention to leave (aOR = 1.64; 95% CI = 1.13-2.37). CONCLUSIONS: During the COVID-19 pandemic, some of the public health workforce experienced negative impacts on their mental health. 33% of the sample indicated an intention to leave their role, which has the potential to exacerbate pre-existing challenges in workforce retention. Study findings create an impetus for policy and practice changes to mitigate risks to mental health and attrition to create safe and healthy working environments for public health workers during public health crises.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".