Translation, cultural adaptation, and validation of the duke activity status index in the hindi language
Bibliographic record
Abstract
Background: The Duke Activity Status Index (DASI) is a validated questionnaire in English to assess the functional capacity (FC) of patients with cardiovascular disease (CVD). Aim: The aim of the study is to translate, cross-culturally adapt, and validate the DASI in Hindi. Settings and Study Design: Observational validation study. Methodology: Different translators translated the DASI into Hindi and then back-translated it into English. Validation for feasibility and psychometric properties of translated questionnaire was done on 200 adults, Hindi-speaking patients with CVD, who were advised exercise testing by a cardiologist. Statistical Analysis: Internal consistency (Cronbach's α) and test-retest reliability (Pearson's correlation coefficient) were calculated. Construct (correlation with the Canadian Cardiovascular Society Classification [CCSC] for angina and exercise capacity with treadmill testing [TMT]) and content validity (time taken to fill the questionnaire, ease of understanding the questionnaire items, and comprehensibility) were calculated.P < 0.05 was considered significant. Results: The Cronbach's α for internal consistency was 0.78, which indicates adequate relatedness among the items of questionnaire, and the test-retest reliability was 0.65 (P < 0.05). A significant correlation between CCSC (r = -0.60) and TMT (r = 0.56) was found. The median time taken by the respondents to fill the questionnaire was 4 min. Of all the respondents, 95.74% of the respondents agreed that the Hindi questionnaire was easy to comprehend and 97.87% patients correlated the translated items to their daily physical activity. Conclusions: The Hindi translated and culturally adapted version of the DASI is reliable, valid, and feasible to assess the FC in the Hindi-speaking CVD patients.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".