Incorporating a standardized judgment assessment for older adults into the healthcare system: A national study
Bibliographic record
Abstract
AIM: Recent research highlights the growing global burden of cognitive decline and dementia in older adults, emphasizing the need for improved methods to assess and support their safety and quality of life. This study builds on prior work to culturally adapt and validate the Verbal Test of Practical Judgment (VPJ) for older adults (VPJ-H) in Israel. This study aimed to validate the assessment in a large and diverse patient sample, and promote its integration into daily healthcare practice. METHODS: This was a cross-sectional study. Participants were recruited from eight healthcare facilities throughout the country. Patients were included if they were aged >60 years, had a Montreal Cognitive Assessment score of ≥13 and were literate in Hebrew, Russian or Arabic. After an interrater agreement process, the VPJ-H was administered to all participants, and compared with standardized functional and cognitive instruments currently used in clinical settings. RESULTS: A total of 133 participants (54% women, mean age 74 years, SD 7.66) were included. A confirmatory factor analysis showed the construct validity of the assessment. No correlation was found with depression, demonstrating discriminant validity. Additionally, positive and significant (P < 0.05) correlations were found between the VPJ-H and the functional and cognitive measures, and between judgement (VPJ-H) and instrumental activities of daily living, showing convergent validity. CONCLUSIONS: This study established the validity of the VPJ-H as a standardized judgment assessment, and, more importantly, took initial steps toward its integration into healthcare systems, highlighting the need for future research on implementation across clinical settings. Geriatr Gerontol Int 2025; 25: 1488-1494.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".