A Systematic Review of the Psychometric Properties of the Geriatric Anxiety Inventory
Bibliographic record
Abstract
Abstract The Geriatric Anxiety Inventory (GAI) is a widely used self-report measure of anxiety symptoms in older adults. Although much research has been conducted on the psychometric properties of the GAI, previous reviews have examined only a small proportion of studies and have not evaluated the methodological quality of this work. In view of this, we conducted a systematic review of the psychometric properties of the GAI and it’s short form (GAI-SF). Relevant studies (N = 31) were retrieved through a search of electronic databases (Pubmed, PsycINFO, CINAHL, EMBASE and Google Scholar) and a hand search. The methodological quality of the included studies was assessed by two independent reviewers using the ‘‘COnsensus-based Standards for the selection of health status Measurement INstruments’’ (COSMIN) checklist. Based on the COSMIN checklist, internal consistency and test reliability were mostly rated as poorly assessed (63% and 72.7% of studies, respectively) and quality of studies examining structural validity was mostly fair (60% of studies). Both the GAI and GAI-SF showed adequate internal consistency and test-retest reliability. Convergent validity indices were highest with measures of generalized anxiety and lowest with instruments that include somatic symptoms. Substantial overlap with measures of depression was reported. While there is no consensus on the factorial structure of the GAI, the short version was found to be unidimensional. Our review therefore suggests that the GAI and GAI-SF have satisfactory psychometric properties while indicating that future efforts should aim to achieve a higher degree of methodological quality.
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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 | 0.021 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".