Cross-cultural adaptation, validity and reliability of the Hausa version of the Neck Disability Index questionnaire
Bibliographic record
Abstract
Background/Aims: The aim of this study was to translate and cross-culturally adapt initiated psychometric tests of the Neck Disability Index (NDI) questionnaire into Hausa language. The prevalence of neck conditions among Hausa patients in Northern Nigeria necessitates the use of the NDI questionnaire. The fact that there was no Hausa version of this instrument has limited its use, hence there is a need for its translation into the Hausa language. Methods: The English version of the NDI questionnaire was translated into Hausa language through a forward and backward translation process. Sixty-two patients were selected for validation of the questionnaire using a purposive sampling technique. The original English version (NDI) and the Hausa version (NDI-H) were administered to the patients and re-administered within the space of one week to ensure validation. Psychometric testing was done to ascertain reliability and construct validity. Findings: The Hausa version of the NDI showed good internal consistency (α= 0.741) and test-retest reliability of r=0.84, p< 0.05. The responsiveness analysis revealed a significant relationship (r = 0.47, p=0.01). Factor analysis revealed a 2-factor 10 items structure which explained the variance of 49.7%. Conclusions: The NDI was successfully translated into Hausa language and validated with good psychometric properties.
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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 | 0.012 |
| 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 | 0.001 |
| 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".