Bearing the Burden of Care: Emotional Burnout Among Maternity Support Workers
Bibliographic record
Abstract
Abstract This research examines effects on emotional burnout among “maternity support workers” (MSWs) that support women in labor (labor and delivery (L&D) nurses and doulas). The emotional intensity of maternity support work is likely to contribute to emotional distress, compassion fatigue, and burnout. This study uses data from the Maternity Support Survey (MSS) to analyze emotional burnout among 807 L&D nurses and 1,226 doulas in the United States and Canada. Multivariate OLS regression models examine the effects of work–family conflict, overwork, emotional intelligence, witnessing unethical mistreatment of women in labor, and practice characteristics on emotional burnout among these MSWs. We measure emotional burnout using the Professional Quality of Life (PROQOL) Emotional Burnout subscale. Work–family conflict, feelings of overwork, witnessing a higher frequency of unethical mistreatment, and working in a hospital with a larger percentage of cesarean deliveries are associated with higher levels of burnout among MSWs. Higher emotional intelligence is associated with lower levels of burnout, and the availability of hospital wellness programs is associated with less burnout among L&D nurses. While the MSS obtained a large number of responses, its recruitment methods produced a nonrandom sample and made it impossible to calculate a response rate. As a result, responses may not be generalizable to all L&D nurses and doulas in the United States and Canada. This research reveals that MSWs attitudes about medical procedures such as cesarean sections and induction are tied to their experiences of emotional burnout. It also demonstrates a link between witnessing mistreatment of laboring women and burnout, so that traumatic incidents have negative emotional consequences for MSWs. The findings have implications for secondary trauma and compassion fatigue, and for the quality of maternity care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".