Fetal health anxiety: development and psychometric properties of the fetal health anxiety inventory
Bibliographic record
Abstract
Objective: To develop a measure to assess fetal health anxiety and examine its factor structure, convergent and divergent validity.Methods: In Study 1, the Short Health Anxiety Inventory-14 item version (SHAI) (Salkovskis et al., Psychol Med. 2002;32:843–853) was adapted for use with pregnant women to examine fetal health anxiety named the Fetal Health Anxiety Inventory (FHAI). Four pregnant women and three subject matter experts (SMEs) reviewed the FHAI. In Study 2, 100 pregnant women completed the FHAI and related self-report measures.Results: In Study 1, both reviewer groups provided feedback directing minor changes to the FHAI. In Study 2, a revised version was used. The revised FHAI demonstrated excellent internal consistency (α = 0.91). Results from an EFA suggested that the FHAI may be conceptualized as a one- or two-factor scale. Convergent (pregnancy-related anxiety [r = 0.56, p = .0001], parental health anxiety [r = 0.53, p = .0001], anxiety [r = 0.57, p = .0001], anxiety sensitivity [r = 0.28, p = .004] and intolerance of uncertainty [r = 0.29, p = .003]) and divergent (parental depression [r = 0.16, p = .12]) validity was evidenced with additional measures of interest.Conclusion: Preliminary findings suggest that the FHAI represents a psychometrically sound instrument to measure the construct of fetal health anxiety. Practical and theoretical implications of the present results are discussed.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".