TRANSLATION, CULTURALADAPTATION AND VALIDATION OF GUJARATI VERSION OF MONTREAL COGNITIVE ASSESSMENT (MOCA) IN OLDER ADULTS
Bibliographic record
Abstract
Context: Mild cognitive impairment (MCI) is a term used to describe a level of decline in cognition which is seen as an intermediate stage between normal ageing and dementia in older adults. The Montreal Cognitive Assessment (MoCA) is a useful screening tool validated for MCI. It has been translated in various languages other than English, including various Indian languages such as Hindi, Malayalam, etc. Aims: For the cognitive screening of Gujarati speaking older adults, the Gujarati version of MoCA is required. The present study, therefore, aimed to develop Gujarati version of the MoCA and investigate its validity and reliability for screening MCI in gujarati speaking older adults. Settings and Design: It is a cross sectional study design. The scale was translated in Gujarati as per suggested guidelines and its psychometric properties were evaluated. Qualitative and quantitative validation through expert review was done and content validity Index (CVI) was calculated. Study participants were 30 older adults. Two estimators of reliability i.e., internal consistency reliability and test retest reliability were evaluated. Statistical analysis and results: The content validity was established through qualitative reviews and with I-CVI of each item on scale more than or equal to 0.78 and SCVI/Ave = 0.93. The scale has good internal consistency, Cronbach's alpha (α) =0.74 and high test-retest reliability, intra class correlation coefcient (ICC) =0.87. Conclusions:The Gujarati translated version of MoCAis a valid and reliable tool for detecting mild cognitive impairment in 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 | 0.009 | 0.020 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".