Validity and reliability of the Chinese Version of the Anxiety Sensitivity Index-3 in healthy adult women
Bibliographic record
Abstract
Objective: To test the validity and reliability of the Chinese version of the Anxiety Sensitivity Index-3( ASI-3) in healthy Chinese adult women. Methods: With authorized permission by the author,the ASI-3 was translated into Chinese and adapted. By convenience sampling method,the Chinese version of ASI-3 was administrated to 309 nonclinical women from Changsha City and its surrounding areas in Hunan Province,and 137 out of them were assessed with the Beck Anxiety Index( BAI) in order to analyze the criterion validity. Four weeks later,the ASI-3 was retested to 50 women to analyze the test-retest reliability. Results: Indices of confirmatory factor analysis met the standard of psychometrics and supported the hypothesis of three-factor model of original ASI-3( χ2/df = 2. 59,NFI = 0. 92,IFI = 0. 95,TLI = 0. 94,CFI = 0. 95,RM SEA = 0. 07). The correlation coefficients between BAI and the total ASI-3 and its subscales ranged from 0. 32 to 0. 42( Ps 0. 01). The Cronbach 's α coefficients were 0. 95 for the total scale and 0. 84- 0. 90 for the three subscales. The test-retest reliability coefficients were0. 86 for the total scale and 0. 76- 0. 82 for the subscales. The mean inter-item correlation coefficient was 0. 54 for the total scale,and ranged from 0. 47- 0. 61 for the three subscales. Conclusion: The Chinese version of the Anxiety Sensitivity Index-3 has good validity and reliability,and could be applied as an effective tool to assess anxiety sensitivity in nonclinical Chinese women.
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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 | 0.003 | 0.008 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".