Why Do Women Go Along with This Stuff?
Bibliographic record
Abstract
PREFACE: Normal childbirth has become jeopardized by inexorably rising interventions around the world. In many countries and settings, cesarean surgery, labor induction, and epidural analgesia continue to increase beyond all precedent, and without convincing evidence that these actions result in improved outcomes (1,2). Use of electronic fetal monitoring is endemic, despite evidence of its ineffectiveness and consequences for most parturients (1,3); ultrasound examinations are too often done unnecessarily, redundantly, or for frivolous rather than indicated reasons (4); episiotomies are still routine in many settings despite clear evidence that this surgery results in more harm than good (5); and medical procedures, unphysiological positions, pubic shaving and enemas, intravenous lines, enforced fasting, drugs, and early mother-infant separation are used unnecessarily (1). Clinicians write and talk about the ideal of evidence-based obstetrics, but do not practice it consistently, if at all. Why do women go along with this stuff? In this Roundtable Discussion, Part 2, we asked some maternity care professionals and advocates to discuss this question.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.025 | 0.024 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".