Translation, Cross-cultural Adaptation and Validation of the Neck Bournemouth Questionnaire: Persian Version
Bibliographic record
Abstract
Introduction: The Neck Bournemouth Questionnaire (NBQ) is being used widely in various countries. This study evaluated the validity and reliability of the Persian version of NBQ. Methods: This methodological study investigated 170 patients with chronic neck pain (NP). The psychometric properties of NBQ were evaluated in two stages. First, the standard scale was translated based on Guillemin’s guidelines for cross-cultural adaption and face validity tested in a pilot group (N=25). Second, it was conducted on a sample of 170 diverse chronic pain patients. Construct validity was determined with convergent validity by short-form McGill pain questionnaire. Then, the reliability was confirmed using Cronbach’s alpha and the intraclass correlation coefficient. Results: The instrument demonstrated a good face validity and the participants made minor changes. A slight change was applied on content validity. In construct validity, Pearson’s correlation coefficient was 0.85, which was statistically significant and showed strong correlation. A Cronbach’s alpha of 0.87 was obtained. This confirmed the remarkable internal consistency and stability (0.92). Conclusion: The Persian version of NBQ showed a good internal consistency and reliability and it could be considered as a valuable tool for assessing patients with cervical pain in Iranian 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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".