A cross-sectional survey of the mental health of midwives in Ontario, Canada: Burnout, depression, anxiety, stress, and associated factors
Bibliographic record
Abstract
PROBLEM: Burnout and the psychological co-morbidities stress, anxiety and depression have a significant impact on healthcare providers, including midwives. These conditions impact the quality of care provided to women, and midwives' ability to remain in the profession. BACKGROUND: There is growing concern regarding the retention of maternity care providers in Canada, particularly midwives. Nationally, 33% of Canadian midwives are seriously considering leaving practice; impacts of the profession on work-life-balance and mental health being commonly cited reasons. Burnout has been shown to contribute to workplace attrition, but little is known concerning burnout among Canadian midwives. AIM: To assess levels of stress, anxiety, depression, and burnout among midwives in Ontario, Canada and potential factors associated with these conditions. METHODS: A cross-sectional survey of Ontario midwives incorporating a series of well-validated tools including the Copenhagen Burnout Inventory and the Depression, Anxiety and Stress Scale. FINDINGS: Between February 5, and April 14, 2021, 275 Ontario midwives completed the survey. More than 50% of respondents reported depression, anxiety, stress, and burnout. Factors associated with poor mental health outcomes included having less than 10-years practice experience, identifying as a midwife with a disability, the inability to work off-call, and having taken a prior mental health leave. DISCUSSION & CONCLUSION: A significant proportion of Ontario midwives are experiencing high levels of stress, anxiety, depression, and burnout, which should be a serious concern for the profession, its leaders, and regulators. Investment in strategies aimed at retaining midwives that address underlying factors leading to attrition should be prioritized.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".