Rates of Interventions in Labor and Birth across Canada: Findings of the Canadian Maternity Experiences Survey
Bibliographic record
Abstract
BACKGROUND: Rates of interventions in labor and birth should be similar across a country if evidence-based practice guidelines are followed. This assumption is tested by comparison of some practices across the 13 provinces and territories of Canada. The objective of this study was to describe the wide provincial and territorial variations in rates of routine interventions and practices during labor and birth as reported by women in the Maternity Experiences Survey of the Canadian Perinatal Surveillance System. METHODS: A sample of 8,244 eligible women was identified from a randomly selected sample of recently born infants drawn from the May 2006 Canadian Census. The sample was stratified by province and territory. Computer-assisted telephone interviews were conducted with participating birth mothers by Statistics Canada on behalf of the Public Health Agency of Canada. Interviews took an average of 45 minutes and were completed when infants were between 5 and 10 months old (9-14 mo in the territories). Completed responses were obtained from 6,421 women (78%). RESULTS: Provincial and territorial variations in rates of routine intervention used during labor and birth are reported. The percentage range of mothers' experience of induction (range 30.9%), epidural (53.7%), continuous electronic fetal monitoring (37.9%), and medication-free pain management during labor (40.7%) are provided, in addition to the use of episiotomy (14.1%) or "stitches" (48.3%), being in a "flat lying position" (42.2%), and having their legs in stirrups for birth (35.7%). Wide variations in the use of most of the interventions were found, ranging from 14.1 percent to 53.7 percent. CONCLUSIONS: Rates of intervention in labor and birth showed considerable variation across Canada, suggesting that usage is not always evidence based but may be influenced by a variety of other factors.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".