A mixed-method study exploring barriers and facilitators to midwives’ mental health in Ontario
Bibliographic record
Abstract
BACKGROUND: There is a paucity of information regarding the mental health of midwives working in Ontario, Canada. Many studies have investigated midwives' mental health around the world, but little is known about how the model of midwifery care in Ontario contributes to or negatively impacts midwives' mental health. The aim of the study was to gain a deeper understanding of factors that contribute to and negatively impact Ontario midwives' mental health. METHODS: We employed a mixed-methods, sequential, exploratory design, which utilized focus groups and individual interviews, followed by an online survey. All midwives in Ontario who had actively practiced within the previous 15 months were eligible to participate. FINDINGS: We conducted 6 focus groups and 3 individual interviews, with 24 midwives, and 275 midwives subsequently completed the online survey. We identified four broad factors that impacted midwives' mental health: (1) the nature of midwifery work, (2) the remuneration model, (3) the culture of the profession, and (4) external factors. DISCUSSION: Based on our findings and the existing literature, we have five broad recommendations for improving Ontario midwives' mental health: (1) provide a variety of work options for midwives; (2) address the impacts of trauma on midwives; (3) make mental health services tailored for midwives accessible; (4) support healthy midwife-to-midwife relationships; and (5) support improved respect and understanding of midwifery. CONCLUSION: As one of the first comprehensive investigations into midwives' mental health in Ontario, this study highlights factors that contribute negatively to midwives' mental health and offers recommendations for how midwives' mental health can be improved systemically.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".