Fine tuning fear of childbirth: the relationship between Childbirth Fear Questionnaire subscales and demographic and reproductive variables
Bibliographic record
Abstract
OBJECTIVE: The objective of the current study was to investigate the relationship between the newly developed Childbirth Fear Questionnaire (CFQ) and demographic and reproductive variables. BACKGROUND: The CFQ was developed in an effort to improve measurement and understanding of women's childbirth fears. To our knowledge the CFQ is the only multidimensional measure of childbirth fears in which (a) multiple domains of childbirth fear are assessed and (b) individual subscales have been psychometrically developed. METHODS: Participants were 643 pregnant women residing in English-speaking countries, recruited via online forums. Participants completed a set of questionnaires, including the multidimensional CFQ, via an online survey. Given the differences in childbirth fear between nulliparous and multiparous women, findings are stratified by parity. RESULTS: Gestational age was largely unrelated to fear of childbirth. Age, income and education were negatively related to fear of childbirth. Assisted vaginal delivery and episiotomy in a previous pregnancy were positively associated with a fear of pain. Self-reported history of traumatic vaginal birth was associated with higher scores on all aspects of fear of childbirth. History of caesarean birth was not generally associated with increased childbirth fears, but women with a prior, self-reported traumatic caesarean birth reported more fear of future caesarean births. CONCLUSIONS: Findings are consistent with previous reports of fear of childbirth. However, the CFQ provides increased specificity with respect to women's childbirth fears. This information is relevant to both education and treatment planning for pregnant women and women wishing to reproduce.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".