Accounting for the Rising Caesarean Section Rate in Canada: What Are the Roles of Changing Needs, Practices, and Incentives?
Bibliographic record
Abstract
In most high-income countries, including Canada, the share of births by Caesarean section (C-section) has risen over the past decades to far exceed World Health Organization recommendations of the proportion justified on medical grounds (15 percent). Although unnecessary C-sections represent an important cost for health care systems, they are not associated with clear benefits for the mother and the child and can sometimes represent additional risks. Drawing on administrative records of nearly four million births in Canada, as well as macro data from the United States and Australia, we provide a comprehensive account of rising C-section rates. We explicitly consider the contributions of the main factors brought forward in the policy literature, including changing characteristics of mothers, births, and physicians as well as changing financial incentives for C-section deliveries. These factors account for at most one-half of the increase in C-section rates between April 1994 and March 2011. The majority of the remaining increase in C-sections over the period occurred in the early 2000s. We suggest that some event or shock in the early 2000s is likely the primary determinant of the recent strong increase in the C-section rate in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".