Why women prefer epidural analgesia during childbirth: The role of beliefs about epidural analgesia and pain catastrophizing
Bibliographic record
Abstract
This study investigated the reasons that might lead women to choose or not choose epidural analgesia as a strategy for the management of pain in childbirth. In our sample 55% of 114 women chose EA. Logistic regression resulted in a statistical model with four unique and independent predictors: Parity status and the fear of the side effects of EA each reduced the odds of choosing EA by half, whereas the desire to have a pain-free childbirth and positive experiences with EA of family and friends each doubled the odds of choosing EA. Pain catastrophizing was not related to EA use. The lack of an interrelationship between pain catastrophizing and EA use is probably due to an ambivalent attitude towards EA in pain catastrophizers. Pain catastrophizing was positively associated with the fear of being overwhelmed by labour pain and tendencies to avoid the pain, but also positively with the fear of pain during the insertion of the EA needle. Pain catastrophizing was also strongly related to recommendations to use EA from others, in particular from the midwife and from the gynecologist. Results are discussed in terms of the social impact of pain catastrophizing.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".