Rasch Analysis Supports the Use of the Pain Self-Efficacy Questionnaire
Bibliographic record
Abstract
BACKGROUND: The Pain Self-Efficacy Questionnaire (PSEQ) is used by physical therapists in clinical practice and in research. However, current understanding of the PSEQ's measurement properties is incomplete, and investigators cannot be confident that it provides unbiased information on patient self-efficacy. OBJECTIVE: The aims of this study were: (1) to investigate the scale properties of the PSEQ using Rasch analysis and (2) to determine whether age, sex, pain intensity, pain duration, and pain-related disability bias function of the PSEQ. DESIGN: This was a retrospective study; data were obtained from 3 existing studies. METHODS: Data were combined from more than 600 patients with low back pain of varying duration. Rasch analysis was used to evaluate targeting, category ordering, unidimensionality, person fit, internal consistency, and item bias. RESULTS: There was evidence of adequate category ordering, unidimensionality, and internal consistency of the PSEQ. Importantly, there was no evidence of item bias. LIMITATIONS: The PSEQ did not adequately target the sample; instead, it targeted people with lower self-efficacy than this population. Item 7 was hardest for participants to endorse, showing excessive positive misfit to the Rasch model. Response strings of misfitting persons revealed older participants and those reporting high levels of disability. CONCLUSIONS: The individual items of the PSEQ can be validly summed to provide a score of self-efficacy that is robust to age, sex, pain intensity, pain duration, and disability. Although item 7 is the most problematic, it may provide important clinical information and requires further investigation before its exclusion. Although the PSEQ is commonly used with people with low back pain, of whom the sample in this study was representative, the results suggest it targets patients with lower self-efficacy than that observed in the current sample.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".