Leave of Absence and Return to Work Among Canadian Midwives Who Experience Mental Health Issues: Pilot Study Findings
Bibliographic record
Abstract
Despite the salience of mental health issues in midwifery, we have a limited knowledge of the experiences of midwives who take a leave due to personal or family-related mental health challenges. Our paper draws on a pilot study that aimed to address this gap in the literature by exploring the factors fostering or impeding midwives’ decision to take a leave and to return back to work. In addition to a scoping review of the academic and grey literature conducted on these issues, we administered a pilot online survey completed by sixteen midwives and conducted interviews with seven midwives. Our findings show that challenging working conditions, common to midwifery, can pose mental health challenges impacting midwives’ working ability and leading to leaves of absence or attrition. We also found that certain demographic factors make midwives more likely to leave their work due to mental health challenges. Our findings suggest that specifically targeted programs and policies might be very helpful in facilitating midwives’ return to work. This article has been peer reviewed.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".