Predictors of a negative labour and birth experience based on a national survey of Canadian women
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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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 | 0.002 |
| 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) | 0.002 | 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".