Too Posh To Push? Comparative perspectives on maternal request caesarean sections in Canada, the US, the UK and Finland
Bibliographic record
Abstract
Purpose – This chapter critically examines the purportedly growing phenomenon of Maternal Request Caesarean Sections (MRCS) and its relative contribution to the rising caesarean section (CS) rates.Methodology – We apply a decentred comparative methodological approach to this problem by drawing upon and comparatively examining empirical data from Canada, the US, the UK and Finland.Findings – We find that the general argument that has emerged within the obstetric community, evidenced in particular by a recent “State of the Science” conference, is that the reduced risks and benefits of MRCS are evenly balanced, thus ethically it could be seen as a valid choice for women. This approach, taken in particular in the North American context, negates the problematic nature of accurately measuring, and therefore assessing the importance of maternal request in addressing rising CS rates. Moreover, although some of the blame for rising CS rates has focused on MRCS, we argue that it has a relatively minor influence on rising rates. We show instead how rising CS rates can more appropriately be attributed to obstetrical policies and practices.Originality – In presenting this argument, we challenge some of the prevailing notions of consumerism in maternity care and its influence on the practice patterns of maternity care professionals.Practical implications – Our argument also calls into question how successful efforts to address MRCS will be in reducing CS rates given its relatively minor influence.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".