Prevalence of and factors associated with burnout inmidwifery: A scoping review
Bibliographic record
Abstract
INTRODUCTION: Midwifery care meets the triple aims of health system improvement, i.e. good health outcomes, high client satisfaction, and low per capita costs. Scaling up access to midwifery care is a global priority yet the growth and sustainability of the profession is threatened by high levels of burnout and attrition. This scoping review provides a comprehensive review of the existing literature on burnout in midwifery, with a focus on prevalence, associated factors and potential solutions. METHODS: Four electronic databases were searched to locate relevant literature up to July 2019. A total of 1034 articles were identified and reduced to 27 articles that met inclusion criteria. We summarize sample sizes, settings, study designs, burnout measures, prevalence of burnout, associated factors and potential solutions, and recommendations. RESULTS: Prevalence of burnout was highest among Australian, Western Canadian and Senegalese midwives and lowest among Dutch and Norwegian midwives. Midwives working in caseload/continuity models reported significantly lower burnout compared to midwives working in other models. We identified 26 organizational and personal factors that were significantly associated with burnout, such as high workload, exposure to traumatic events, and fewer years in practices. Organizational support to improve work-life balance and emotional well-being, as well as more continuing education to raise awareness about burnout and how to cope with it, emerged as common strategies to prevent and address burnout. CONCLUSIONS: Burnout is a serious and complex occupational phenomenon. More qualitative research is needed in this area, to better understand the lived experience of burnout.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".