46 Comparison of Anxiety Measures in a Memory Clinic Sample
Bibliographic record
Abstract
Objective: As the presentation of anxiety may differ between younger and older adults, it is important to select measures that accurately capture anxiety symptoms for the intended population. The 21-item Beck Anxiety Inventory (BAI) is widely used; however, its high reliance on somatic symptoms may result in artificial inflation of anxiety ratings among older adults, particularly those with medical conditions. The 30-item Geriatric Anxiety Scale (GAS) was specifically developed for older adults and has shown strong psychometric properties in community-dwelling and long-term care samples. The reliability and validity of the GAS in a memory clinic setting is unknown. The present study aimed to compare the psychometric properties of the GAS and the BAI in a memory disorder clinic sample. Participants and Methods: Participants included 35 older adults (age=73.3±5.0 years; edu=15.3±2.8 years; 42% female; 89% non-Hispanic white) referred for a neuropsychological evaluation in a memory disorders clinic. In addition to the GAS and BAI, the Geriatric Depression Scale (GDS) and Montreal Cognitive Assessment (MoCA) were included. Cutoffs for clinically significant anxiety were based on published data for each measure. A dichotomous anxiety rating (yes/no) was created to examine inter-measure agreement; minimal anxiety was classified as “no” and mild, moderate and severe anxiety were classified as “yes.” Internal scale reliability was examined using Cronbach’s alpha. Convergent and discriminant validity were examined using Spearman rank correlation coefficients. Frequency distributions determined the proportion of yes/no anxiety ratings, and a McNemar test compared the proportion of anxiety classifications between the two measures. Results: Both measures had excellent internal consistency (BAI: a=.88; GAS: a=.94). The BAI and GAS were highly correlated with each other (r=.79, p<.001) and positively correlated with a depression measure (BAI-GDS: r=.51, p=.002; GAS-GDS: r=.53, p=.001). Discriminant validity was supported by lower correlations between the anxiety measures and cognition (BAI-MoCA: r=.38, p=.061; GAS-MoCA: r=.34, p=.098). The BAI classified 14 participants as having anxiety (40%) and 21 participants as not having anxiety (60%), whereas the GAS classified 21 participants as having anxiety (60%) and 14 participants as not having anxiety (40%). The proportion of anxiety classifications were significantly different between the two measures (p =.016). For 28 participants (80%), there was agreement between the anxiety ratings. Seven participants (20%) were classified as having anxiety by the GAS, but not by the BAI; GAS items related to worry about being judged or embarrassed may contribute to discrepancies, as they were frequently endorsed by these participants and are unique to the GAS. Conclusions: Results support that both anxiety measures have adequate psychometric properties in a clinical sample of older adult patients with memory concerns. It was expected that the BAI would result in higher classification of anxiety due to reliance on somatic symptoms; however, the GAS rated more participants as having anxiety. The GAS may be more sensitive to detecting anxiety in our sample, but formal anxiety diagnoses were not available in the current dataset. Future research should examine the diagnostic accuracy of the GAS in this population. Overall, preliminary results support consideration of the GAS in memory disorder evaluations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".