The STAI-Y trait scale: psychometric properties and normative data from a large population-based study of elderly people
Bibliographic record
Abstract
BACKGROUND: Whereas the State-Trait Anxiety Inventory (STAI-Y) is probably the most widely used self-reported measure of anxiety, the lack of current norms among elderly people appears to be problematic in both a clinical and research context. The objective of the present study was to provide normative data for the STAI-Y trait scale from a large elderly cohort and to identify the main sociodemographic and health-related determinants of trait anxiety. METHODS: The STAI-Y trait scale was completed by 7,538 community-dwelling participants aged 65 years and over from the "Three City" epidemiological study. Trained nurses and psychologists collected information during a face-to-face interview including sociodemographic characteristics and clinical variables. RESULTS: The scale was found to have good internal consistency (Cronbach's α = 0.89). Norms were stratified for gender and educational level differentiating persons with and without depressive symptoms. Multivariate linear regression found the STAI-Y trait score to be significantly associated with female gender, psychotropic medication use, higher depressive symptoms, higher cognitive complaints, and with an interaction between subjective health and marital status. Age was not associated with the total score. CONCLUSION: This study provides norms for the STAI-Y trait scale in the general elderly population which are of potential use in both a clinical and research context. The present results confirm the importance of several factors previously associated with higher trait anxiety in the elderly. However, more research is needed to better understand the clinical specificities of anxiety in the elderly and the improvement of assessment.
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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 | 0.007 |
| 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 | 0.000 |
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".