Cross-cultural Adaptation and Validation of Hindi Version of Roland Morris Disability Questionnaire for Chronic Low Back Pain in Indian Population
Bibliographic record
Abstract
STUDY DESIGN: A cross-cultural adaptation, validation, and psychometric analysis. OBJECTIVE: The main aim was to assess the validity and reliability of the Hindi version of the Roland Morris Disability Questionnaire (Hi-RMDQ) for chronic low back pain. SUMMARY OF BACKGROUND DATA: Roland Morris Disability Questionnaire is a standardized, self-administered tool for disability assessment in patients with chronic low back pain. However, its Hindi version has not been validated. MATERIALS AND METHODS: Cognitive debriefing was carried out with 10 patients to ensure the comprehensibility of the Hi-RMDQ. Following this, 120 patients were asked to complete the finalized questionnaire along with the modified Oswestry Disability Index Questionnaire, Quebec Back Pain Disability Score, and the Verbal Numeric Rating Scale. The patients were then asked to again fill out the finalized questionnaire after 72 hours. The internal consistency and retest reliability of the Hindi translated version of the questionnaire was tested. Its correlation with the other scores was also analyzed. RESULTS: The translated questionnaire showed excellent internal consistency (Cronbach α=0.989) and excellent retest reliability (intraclass correlation coefficient=0.978). There was a positive and statistically significant association between the Hi-RMDQ, modified Oswestry Disability Index Questionnaire ( r =0.807; P <0.01), Quebec Back Pain Disability Score ( r =0.839; P <0.01), and Verbal Numeric Rating Scale ( r =0.713; P <0.01). CONCLUSION: The Hi-RMDQ version is an easy-to-use, acceptable, reliable, and valid tool to measure disability in the Indian population with nonspecific back pain with or without leg pain. LEVEL OF EVIDENCE: 3.
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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 | 0.006 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".