Health care workers’ perceptions of episiotomy in the era of respectful maternity care: a qualitative study of an obstetric training program in Mexico
Bibliographic record
Abstract
BACKGROUND: Episiotomy in Mexico is highly prevalent and often routine - performed in up to 95% of births to primiparous women. The WHO suggests that episiotomy be used in selective cases, with an expected prevalence of 15%. Training programs to date have been unsuccessful in changing this practice. This research aims to understand how and why this practice persists despite shifts in knowledge and attitudes facilitated by the implementation of an obstetric training program. METHODS: This is a descriptive and interpretative qualitative study. We conducted 53 pre and post-intervention (PRONTO© Program) semi-structured interviews with general physician, gynecologists and nurses (N = 32, 56% women). Thematic analysis was carried out using Atlas-ti© software to iteratively organize codes. Through interpretive triangulation, the team found theoretical saturation and explanatory depth on key analytical categories. RESULTS: Themes fell into five major themes surrounding their perceptions of episiotomy: as a preventive measure, as a procedure that resolves problems in the moment, as a practice that gives the clinician control, as a risky practice, and the role of social norms in practicing it. Results show contradictory discourses among professionals. Despite the growing support for the selective use of episiotomy, it remains positively perceived as an effective prophylaxis for the complications of childbirth while maintaining control in the hands of health care providers. CONCLUSIONS: Perceptions of episiotomy shed light on how and why routine episiotomy persists, and provides insight into the multi-faceted approaches that will be required to affect this harmful obstetrical practice.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".