Factors related to childbirth satisfaction
Bibliographic record
Abstract
BACKGROUND: A woman's satisfaction with the childbirth experience may have immediate and long-term effects on her health and her relationship with her infant, but there is a lack of current research in this area. AIM: This paper reports a study to examine multiple factors for their association with components of childbirth satisfaction and with the total childbirth experience. METHOD: A correlational descriptive study was conducted with 60 low-risk postpartum women, aged 18-46 years, with uneventful vaginal deliveries of healthy full-term infants at two medical centres in the south-eastern United States. The Labor Agentry Scale, McGill Pain Questionnaire and Mackey Childbirth Satisfaction Rating Scale and a background questionnaire were completed by women. Obstetrical data were collected from the medical record. FINDINGS: Personal control was a statistically significant predictor of total childbirth satisfaction (P = 0.0045) and with the subscale components of satisfaction (self, partner, baby, nurse, physician and overall). In addition, having expectations for labour and delivery met was a significant predictor of satisfaction with own performance during childbirth. CONCLUSIONS: Personal control during childbirth was an important factor related to the women's satisfaction with the childbirth experience. Helping women to increase their personal control during labour and birth may increase the women's childbirth satisfaction.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".