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 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 | 0.001 |
| 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) | 0.002 | 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".