Experiences and satisfaction with intrapartum care: a comparison of normal weight women to obese women
Bibliographic record
Abstract
Obesity is a steadily growing problem, and has both physiological and psychological consequences during pregnancy. Obese women may face discrimination which could shape their perceptions of maternity care. To date, few studies have studied the influence of body weight on patient satisfaction with care. The objectives of this study were: (1) to compare childbirth experiences and satisfaction with intrapartum care of normal weight (BMI between 18.5 and 24.9 kg/m2) and obese (BMI greater than or equal to 30.0 kg/m2) women and (2) to determine factors associated with satisfaction with intrapartum care. Guided by Barker’s (1997) pragmatic model of patient satisfaction, a descriptive comparative and correlational design was used to examine the relationship between childbirth experiences, weight discrimination, and satisfaction with intrapartum care among normal weight and obese women. Postpartum primiparous women (N = 138) in two Winnipeg hospitals completed a questionnaire package and had their chart reviewed (70 normal weight, 68 obese weight). Results: Using independent t-test, no significant differences in satisfaction with intrapartum care or childbirth experiences were found in the two weight groups. In the linear multiple regression model, perceived weight discrimination during labour and delivery was negatively associated (β = -5.78, p = 0.032), while professional support (β = 13.11, p < .001) and perceived control and safety (β = 3.25, p = 0.032) were positively associated with satisfaction with intrapartum care. Understanding factors that influence satisfaction with intrapartum care will assist healthcare providers and administrators to improve satisfaction in all women regardless of their weight.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".