Development of the Marathi Version of the Quebec Back Pain Disability Scale: A Cross-Cultural Adaptation and Validation in Patients With Chronic Low Back Pain
Bibliographic record
Abstract
Abstract Background: The Quebec Back Pain Disability Scale (QBPDS) is a more commonly used measure to assess the disability in low back pain (LBP) patients. It is important to administer patient-reported outcomes in the patient’s native language to enhance successful outcomes. The objective of this study was to translate, culturally adapt, and validate QBPDS into Marathi language and establish psychometric properties in chronic LBP patients. A prospective, cross-sectional study was conducted in a tertiary health care center. Materials and Methods: The QBPDS was translated and culturally adapted into Marathi (QBPDS-M) following standardized guidelines. A total of 75 chronic low back patients participated in the study. The construct validity of QBPDS-M was evaluated using exploratory factor analysis. Convergent validity was assessed using the Pearson correlation analysis between the QBPDS-M and Roland-Morris Disability Questionnaire (RMDQ) and the Numeric Pain Rating Scale (NPRS). Internal consistency and test–retest reliability was computed using Cronbach’s alpha and intra-class correlation coefficient (ICC), respectively. Results: Construct validity was established revealing the 6-factor structure of QBPDS-M with 60.62% of the total variance. Content validity was confirmed with no floor or ceiling effects. Convergent validity showed a moderate correlation with RMDQ ( r = 0.68 and P = 0.000) and NPRS ( r = 0.345 and P = 0.002). The QBPDS-M demonstrated excellent internal consistency (Cronbach’s α = 0.893) and good test–retest reliability (ICC 2,1 = 0.862). Conclusion: Psychometric analysis of QBPDS-M demonstrated satisfactory construct validity, internal consistency, and test–retest reliability. It can be utilized for clinical and research purposes to assess the functional disability in Marathi-speaking chronic LBP 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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".