Psychometric Properties of the “Lasher and Faulkender Anxiety about Aging Scale” among Iranian older people
Bibliographic record
Abstract
Abstract BackgroundAssessing anxiety in the elderly and the factors affecting this phenomenon will help the health care providers to provide appropriate and effective support and health care services for older adults. The aim of the present study was to assess the psychometric properties of the Aging Scale (AAS) among Persian speaking older adults.Method:A sample of 703 community-dwelling older adults was recruited for the study. A 'forward-backward' translation procedure was conducted to develop the Iranian version of the AAS. Confirmatory factor analysis (CFA) and Rasch model were then used for construct validity, and GHQ-12 and MSPSS were utilized for assessing concurrent validity of the AAS.ResultThe study participants included 416 (59.2%) men and 287 (40.8%) women with an average age of 69.4 years (SD D 8.11). Cronbach’s alpha for Fear of Old People, Psychological Concerns, Physical Appearance, Fear of Losses and the overall score was 0.881, 0.705, 0.748, 0.768 and 0.77, respectively. Applying CFA, it was found that the four original factors model was the best solution with 0.55 of the total variance. The result of the CFA indicated that this four-factor model had a good fit to the data. The results were then confirmed by Rasch analysis. Moreover, the AAS was significantly correlated with MSPSS (r=-0.395, p < 0.001) and GHQ_12 (r = 0.238, p < 0.001).ConclusionThe Persian version of the AAS was found to be valid and reliable for measuring anxiety of ageing among Persian speaking elderly populations.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".