Predictors of a negative labour and birth experience based on a national survey of Canadian women
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Bibliographic record
Abstract
BACKGROUND: A negative birth experience has been shown to have a significant impact on the well-being and future choices of mothers. The objective of this study was to assess the prevalence of, and identify the risk factors associated with a negative birth experience for women in Canada. METHODS: The study was based on secondary data analysis of the Maternity Experiences Survey (MES), a Canadian population database administered to 6,421 Canadian women in 2006. The examined outcome - negative birth experience - was derived from mothers' self-report of overall labour and birth experience. Independent variables were maternal demographics, health characteristics, pregnancy-related characteristics, and birth characteristics. Multivariable logistic regression analysis was performed to determine the significant predictors of negative birth experience. Adjusted Odds Ratios (AOR) and 95 % Confidence Intervals (CI) are reported. RESULTS: Negative birth experience was reported among 9.3 % of women. The main significant predictors of a negative birth experience included older age (AOR 2.29, 95 % CI, 1.03-5.07), violence experienced in the past two years (AOR, 1.62, 95 % CI, 1.21-2.18), poor self-perceived health (adjusted OR, 1.95, 95 % CI, 1.36-2.80), prenatal classes attended (adjusted OR, 1.36, 95 % CI, 1.06-1.76), unintended pregnancy (adjusted OR, 1.30, 95 % CI, 1.03-1.63), caesarean birth (AOR, 1.65, 95 % CI, 1.32-2.06), and neonate admission to intensive care (AOR, 1.40, 95 % CI, 1.08-1.82). CONCLUSION: Significant predictors of a negative labour and birth experience were identified through this study, a first in the Canadian context. These findings suggest future research directions and provide a basis for the design and evaluation of maternal health policy and prevention programs.
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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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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 it