Prenatal fear of childbirth and anxiety sensitivity
Bibliographic record
Abstract
OBJECTIVE: Fear of childbirth (FOC) or what is historically referred to as tokophobia (a phobic state where a woman avoids childbirth despite desperately wanting a baby), is known to complicate the delivery process. In this study, the relationship of Anxiety Sensitivity (AS) to FOC was examined given that AS is a risk factor for other fears. Specifically, the contribution of three AS dimensions (physical, psychological or social concerns) relative to other factors (e.g., parity of the mother, trait anxiety) in accounting for FOC was explored. METHODS: Women in their final 4 months of pregnancy (n = 110) completed the Anxiety Sensitivity Index, the State-Trait Anxiety Inventory-Trait Scale and the Wijma Delivery Expectancy/Experience Questionnaire. RESULTS: Most demographic variables were non-significant in predicting FOC with the exception of participants' parity. Multiple regression analysis revealed that AS-physical concerns significantly predicted elevated FOC even after controlling for parity and trait anxiety; higher levels of AS-physical concerns, higher trait anxiety, and expecting a first child all independently predicted greater FOC. CONCLUSION: Variance in FOC is explained, in part, by AS-physical concerns. Further, AS-physical concerns are distinct from trait anxiety in predicting FOC. Similar to other fears, the results support the possibility that AS may be a risk factor for elevated FOC.
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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 | 0.000 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".