Client‐Related Factors Associated with a “Less than Good” Experience of Midwifery Care during Childbirth in the Netherlands
Bibliographic record
Abstract
BACKGROUND: A "less than good" experience during childbirth can affect a mother's early interaction with her child and may significantly influence a woman's emotional well-being. In this study, we focus on clients who experienced midwifery care provided during childbirth as "less than good" care. The aim of this study was to understand the relationship between client-related factors and the experience of midwifery care during childbirth to improve this care. METHODS: This study was part of the "DELIVER study" where mothers report on the care they received. We used generalized estimation equations to control for correlations within midwife practices. Forward multivariate logistic regression analyses were conducted to model the client-related factors associated with the experienced midwifery care during childbirth. RESULTS: We included the responses of 2,377 women. In the multivariable logistic regression model, odds of reporting "less than good care" were significantly higher for women who experienced an unplanned cesarean birth (OR 2.21 [CI 1.19-4.09]), an instrumental birth (OR 1.55 [CI 1.08-2.23]), and less control during the dilation phase (OR 0.98 [CI 0.97-0.99]) and pushing phase (OR 0.98 [CI 0.97-0.99]). DISCUSSION: Birth-related factors were more likely than maternal characteristics to be associated with the experience of midwifery care during childbirth. We conclude that there is room for midwives to improve their care for women during childbirth particularly in improving the patient centeredness of the care provider, using strategies to enhance sense of control, and focusing on the particular needs of those who experience instrumental vaginal or unplanned cesarean births.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".