Psychometric properties of the Oswestry disability index: Rasch analysis of responses in a work-disabled population
Bibliographic record
Abstract
BACKGROUND: The Oswestry disability index (ODI) is the most widely used measure of perceived disability for low back conditions. It has been adopted without adaptation in functional capacity evaluation (FCE). Rigorous testing of the ODI with modern psychometric methods, in this setting, is warranted. OBJECTIVE: To determine the psychometric properties of the ODI in FCE: unidimensionality; differential item functioning; item coverage and to identify poorly functioning items, allowing for improvement of these items and recalibration of the scale. METHODS: Rasch analysis, specifically Masters' partial credit model, was conducted on data. PARTICIPANTS: 133 work-disabled individuals presenting for FCE in northern British Columbia, Canada. RESULTS: All items had one poorly functioning option. Items were rescaled from six categories to five, improving the psychometric properties of the ODI as a unidimensional (disability due to back pain) scale. Item difficulty range is sufficient for a population with mild to severe disability. CONCLUSION: Although two of the ten ODI items functioned marginally unsatisfactorily in the unrevised state, the 5-option revised ODI appears superior. Use in clinical settings across a broad spectrum of disability levels could help establish its psychometric properties. Health professionals should be aware that the ODI may perform differently depending on client 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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".