Assessment of Anxiety in Older Adults: A Reliability Generalization Meta-Analysis of Commonly Used Measures
Bibliographic record
Abstract
We conducted a reliability generalization meta-analysis of the 12 most commonly used measures of anxiety in older adults aged 65 and older. Of the 136 articles considered for inclusion, only 24% of published studies reported reliability coefficients from their original data collection. We used 63 reliability coefficients from 51 articles and 16,183 individuals to provide internal consistency reliability estimates for this meta-analysis. We present the average score reliabilities for each of the 12 measures, characterize the variance in score reliabilities across studies, and consider sample and study characteristics that are predictive of score reliability. We discuss the importance of considering factors specific to the assessment of older adults (e.g., the frequency of a comorbid medical condition) as well as the importance of conducting sample specific reliability analyses. Recommendations are provided for researchers and clinicians choosing a measure of anxiety for use with older adults.
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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 | 0.091 | 0.172 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.044 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".