Confidence in knowledge, childbirth fear, and preference for cesarean birth among Polish women: a cross-sectional study
Bibliographic record
Abstract
In Poland, 48 in 100 babies are born by cesarean section, which is among the highest rate of cesarean birth (CB) in the Organization for Economic Cooperation and Development (OECD) countries. Several factors are linked to higher CB rates, including childbirth fear prior to pregnancy (CFPP), and physician versus midwifery led models of care. In order to decrease CB rates, it is crucial to understand modifiable factors that are associated with childbirth preferences. In this study, we tested how confidence in knowledge of pregnancy and birth was related to: (i) fear of childbirth, preference for: (ii) mode of birth and (iii) prenatal care provider type. We recruited 782 women aged 18-35 (mean 24.7, SD 3.19) who had never been pregnant but desired to have at least one child in the future. Women with moderate and high levels of confidence in knowledge had lower odds of high fear of childbirth compared to women with low levels of confidence (aOR = 0.57, 95% CI: 0.39-0.83 and aOR = 0.54, 95% CI: 0.33-0.88, respectively). Neither moderate nor high levels of confidence in knowledge were associated with a preference for CB (aOR = 1.10, 95% CI: 0.73-1.67 and aOR = 0.92, 95% CI: 0.55-1.55, respectively) compared to low levels. In addition, women with high levels of confidence in knowledge had significantly lower odds of preferring obstetricians (aOR = 0.49, 95% CI: 0.26-0.89), compared to midwives. Our study provides evidence that confidence in knowledge is related to fear of childbirth and prenatal care provider preferences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".