Fear, Risk, and the Responsible Choice: Risk Narratives and Lowering the Rate of Caesarean Sections in High-income Countries
Bibliographic record
Abstract
In Canada, as elsewhere in the world, caesarean sections are the most common surgical procedure performed in hospitals annually. Recent national statistics indicate 28% of infants in Canada are born by c-section while in the United States that number rises to 33%. This is despite World Health Organization recommendations that at a population level only 10-15% of births warrant this form of medical intervention. This trend has become cause for concern in recent decades due to the short and long-term health risks to pregnant women and infants, as well as the financial burden it places on public health care systems. Others warn this trend may result in a collective loss of cultural knowledge of a normal physiological process and, in the process, establish a new "normal" childbirth. Despite a range of interventions to curb c-section rates-enhanced prenatal care and innovation in pregnancy monitoring, change in hospital level policies, procedures and protocols, as well as public education campaigns-they remain stubbornly resistant to stabilization, let alone, reduction in high-income countries. We explore-through a review of the academic and grey literature-the role of cultural and social narratives around risk, and the responsibilization of the pregnant woman and the medical practitioner in creating this kind of resistance to intervention today.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".