Psychometric validation of the authorized Polish version of the Neck Disability Index
Bibliographic record
Abstract
Grażyna Guzy*a, Howard Vernonb, Romuald Polczykc & Malwina Szpitalakca Department of Physiotherapy, University School of Physical Education CracowPolandb Canadian Memorial Chiropractic College TorontoCanadac Institute of Psychology, Jagiellonian University CracowPolandAddress for correspondence: Grażyna GuzyAkademia Wychowania Fizycznego, Katedra Fizjoterapii Al. Jana Pawła II 78/310A, 31-571 KrakówPoland. Tel: +48 12 683 11 67. E-mail: grazyna.guzy@fizjoterapia.euAbstractPurpose: Psychometric validation of the authorized Polish version of the Neck Disability Index (NDI). Methods: Ninety-five patients with neck pain were enrolled. Reliability was assessed through Cronbach’s alpha, split-half reliability, intra-class correlation (ICC) and agreement between measures with limits of agreement using the interval of 48 hours. Validity was determined by the Pearson correlation of the NDI with VAS. Responsiveness included mainly Pearson correlations of score changes on the NDI with the Global rating of change (GRC) scale. Minimal detectable change (MDC) and factor analysis were performed. The cut-point for the change, with its sensitivity and specificity, and the area under the curve were determined with the receiver operating characteristic (ROC) curve analysis. Results: Cronbach’s alpha and split-half reliability were satisfactory. The ICC was 0.99. Bland and Altman analysis indicated an acceptable agreement between the measures. The correlation between the NDI and VAS was 0.55. Responsiveness estimated by the correlations between change scores of the NDI and GRC was −0.73 and −0.56. The MDC was 5.96. Factor analyses demonstrated a two-factor structure. The cut-point for detecting a change was 6.5. The sensitivity was 90% and specificity was 81%. Conclusions: The Polish version of the NDI showed good psychometric properties. It can be used both in clinical and research practice. Implications for RehabilitationThe Polish version of the NDI, developed by MAPI Research Institute, was researched on a sample of 95 patients with neck pain.The psychometric properties of the adapted version of the NDI were satisfactory.The Polish version of the NDI proved to be useful in clinical practice as well as in research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".