Psychometric properties of the Chinese version of the fathers’ fear of childbirth scale: A cross-sectional study
Bibliographic record
Abstract
Background and Aim Fear of childbirth (FOC) is one of the most common mental health concerns among expectant fathers, which can cause adverse consequences for themselves and their families. A valid and accurate tool is the key to the identification of FOC. This study aimed to translate and culturally adapt the fathers’ fear of childbirth scale (FFCS) into simplified Chinese and test the scale’s psychometric properties among expectant fathers in mainland China. Methods Researchers obtained translation permission and followed the multiphase translation guidelines to develop the Chinese version of the fathers’ fear of childbirth scale (C-FFCS). Relevant psychometric properties were selected for the scale’s psychometric validation on the basis of the Consensus-based Standards for the Selection of Health Status Measurement Instruments checklist. In this cross-sectional study, two samples of expectant fathers were collected in a university-affiliated hospital in Hangzhou between September and October 2022. Results A total of 381 expectant fathers completed the C-FFCS, resulting in an effective response rate of 95.6%. The C-FFCS is a 3-factor structure consisting of 16 items, which explained 66.374% of the total variance. The content validity index of items ranged from 0.833 to 1.00, and the scale-level content validity index was 0.931. The confirmatory factor analysis confirmed the scale’s 3-factor structure. Evidence of convergent validity (average variance extracted = 0.508–0.780) as well as discriminant validity offered excellent psychometric support. The Cronbach’s α coefficient, McDonald’s ω coefficient, intraclass correlation coefficient, Spearman-Brown coefficient, and Guttman split-half coefficient are within the satisfactory range (> 0.80). Significant correlations between the scores of the C-FFCS and Childbirth Attitude Questionnaire ( r = 0.658, p < 0.01) and Fear of Birth Scale ( r = 0.555, p < 0.01) both revealed good concurrent validity. The structure of C-FFCS was invariant across different parity groups, with no floor and ceiling effect. Conclusion The C-FFCS was demonstrated to be a sound instrument with good reliability and validity for measuring Chinese expectant fathers’ FOC levels. However, further studies are advocated to verify the C-FFCS among a larger sample that is more representative of the Chinese expectant father population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".