Short STAI-Y anxiety scales: validation and normative data for elderly subjects
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to develop short forms of the STAI-Y trait and state scales and associated norms suitable for the screening of anxiety in elderly populations. METHOD: This study was based on population-based cohorts of older persons from two epidemiological French studies that each included one subscale of the STAI-Y, i.e. state and trait anxiety scales. For both scales, the most discriminative items were retained and their factorial structure was examined using principal components analysis. Internal consistency (Cronbach's alpha) was estimated and cut-offs and norms were computed. RESULTS: A 10-item STAI-Y version produced scores similar to those obtained with the full form of the STAI-Y. The factorial structure of the shortened form is comparable to that of the full scales. Results showed good internal consistency (alpha coefficients were 0.92 and 0.85 for short STAI-Y state and trait scales, respectively). Moreover, both short STAI-Y state and trait scales correctly classified 88% of the participants using a cut-off point of 23. Norms for both short trait and state anxiety scales are provided according to age, gender, educational level and depressive symptoms. CONCLUSION: Both shortened scales have similar factorial structure and internal consistency to the longer scales and classify anxious/non-anxious elderly with acceptable accuracy. The shorter form is likely to be more acceptable to elderly persons through reduction of fatigue effects.
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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 | 0.007 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".