Midwives’ Intention to Stay in the Profession: Results of a Mixed- Methods Pan-Canadian Study
Bibliographic record
Abstract
Midwifery is a rewarding career, but a considerable number of midwives decide to leave the profession early. To explore how to improve retention among midwives in Canada, we conducted a national study funded by the Canadian Institutes of Health Research. Applying a mixed-method design, we explored (a) practicing midwives’ intention to stay in the profession and (b) factors that shape midwives’ professional experiences and their job satisfaction. We had 720 midwives respond to our 2018 online survey. To better understand midwives’ working experiences, we also conducted qualitative, semi-structured interviews with 29 midwives across Canada. Our findings suggest that while the majority (95%) of midwives feel pride from their work and enjoy it, about a third of midwives who took part in our study considered leaving the profession. Qualitative and quantitative data suggest that challenging working conditions, inadequate remuneration, and a policy context in which midwives work may impact their decision to leave the job. In conclusion, we identify actionable strategies for workforce policy planning that can improve working conditions for Canadian midwives and increase retention. This article has been peer reviewed.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| 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".