Hindi Translation, Cross-cultural Adaptation and Validation of Chedoke McMaster Stroke Assessment (CMSA) Scale: A Cross-sectional Study
Bibliographic record
Abstract
Introduction: Stroke is one of the leading causes of death and long-term disability worldwide, emphasising the need for effective rehabilitation strategies. The Chedoke McMaster Stroke Assessment (CMSA) is a reliable and valid measure developed in Canada used to assess both impairment and activity levels in persons with stroke. The widespread use of Hindi, there is no Hindi translation of the CMSA. Developing a culturally and linguistically appropriate version of the CMSA for Hindi speakers could enable rehabilitation personnel to evaluate change in the patient’s motor control and functional ability. Aim: This study focussed on translating and adapting the CMSA into Hindi to ensure its relevance and effectiveness for assessing stroke recovery for patients in India by Hindi-speaking rehabilitation specialists. Materials and Methods: We obtained permission from the original author of the CMSA to translate the tool into Hindi. The translation process adhered to recognise guidelines for crosscultural adaptation. Two bilingual experts, one with a medical background and the other a linguistic specialist, independently translated the CMSA into Hindi. The translations were combined and back-translated into English by independent translators to ensure consistency with the original tool. To ensure content validity, we used the Delphi method to assess the relevance of each item in the scale. The experts evaluated each item on a 4-point scale, and the Item-Level Content Validity Index (I-CVI) and Scale-Level Content Validity Index Average (S-CVI/Ave) were calculated. Results: There is an evidence of its criterion validity which demonstrated it as high degree of linguistic and cultural equivalence. The Hindi CMSA achieved an I-CVI of 0.98985, an S-CVI/Ave of 0.98985, and an S-CVI/UA of 0.881944, indicating strong evidence of its validity. Conclusion: The Hindi CMSA has been culturally adapted and validated for evaluating stroke-related impairments and functional activity in Hindi-speaking healthcare environments. This version will enhance the ability of rehabilitation personnel in conducting clinical assessments and customising rehabilitation strategies for this population.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".