Translation and Cross-Cultural Adaptation of Nepali Version of the Montreal Cognitive Assessment
Bibliographic record
Abstract
BACKGROUND: Montreal Cognitive Assessment is widely used in stroke to detect cognitive impairment. The superiority of it over other outcome measures has been well established. It has been cross-culturally translated and has shown excellent psychometric properties. To assess the intervention effect on cognition of Nepalese individuals with stroke using the Montreal Cognitive Assessment, an adapted Nepali version is required as the Nepalese cultural context and language are completely different than the original was developed. Thus, the objective of this study is to translate and cross-culturally adapt Montreal Cognitive Assessment in the Nepali language and see its test-retest reliability and internal consistency. METHODS: After translating and cross-culturally adapting the Montreal Cognitive Assessment into Nepali using Beaton guidelines. Its Nepali version was administered to 28 individuals with stroke twice keeping the interval of two weeks. Test-retest reliability and internal consistency were assessed using the Intraclass correlation coefficient and Cronbach's alpha. RESULTS: The Montreal Cognitive Assessment was translated into Nepali with significant cultural adaptations and the Nepali version demonstrated excellent psychometric properties as hypothesized. The test-retest reliability and internal Consistency were excellent. The Intraclass correlation coefficient of the total score was 0.990 and Cronbach's alpha value was 0.994 for total scores. CONCLUSIONS: The Nepali version of Montreal Cognitive Assessment is reliable to use as a diagnostic tool for detecting cognitive impairment in patients with stroke. It is comprehensive, easy to administer and culturally appropriate.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".