Evaluation of the Structural Validity of the Work Instability Scale Using the Rasch Model
Bibliographic record
Abstract
To use Rasch analysis to examine the measurement properties of the 23-item version of the Work Instability Scale (WIS-23) in a sample of worker compensation claimants with upper extremity disorders. Secondary data analysis on the data retrieved from a cross-sectional study. Tertiary care hospital. Patients (N=392) attending a specialty clinic for workers with upper limb injuries at a tertiary hospital were prospectively enrolled. Not applicable. WIS-23. The study sample contained 392 participants between the ages of 19 and 73 years (mean, 47.0±10.5y). There were 148 (37.8%) women, 182 (46.4%) men, and 62 (15.8%) participants for whom sex identification was unavailable. The initial WIS data analysis showed significant misfit from the Rasch model (item-trait interaction: χ2=293.52; P<.0001). Item removal and splitting were performed to improve the model fit, resulting in a 20-item scale that met all assumptions (χ2=160.42; P=.008), including unidimensionality, local independence of items, and the absence of differential item function based on age, sex of respondents, employment type, and affected upper extremity area across all tested factors. With the application of Rasch analysis, we refined the WIS-23 to produce a 20-item WIS for work-related upper extremity disorders (WIS-WREUD). The 20-item WIS-WREUD demonstrated excellent item and person fit, unidimensionality, acceptable person separation index, and local independency. The WIS-20 may provide better measurement properties, although longitudinal psychometric evaluations are needed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".