Establishing cut-offs for the Pain Self-Efficacy Questionnaire for people living with chronic pain
Bibliographic record
Abstract
Introduction: This study aimed to establish clinically informed sub-group cut-offs for the Pain Self-Efficacy Questionnaire (PSEQ) by examining correlations and main and interaction effects of the PSEQ with other clinical measures and demographic data in a sample of individuals who attended a four-week interdisciplinary chronic pain management program. Methods: A sample of 189 patients (69 of whom were referred by Veterans Affairs Canada) who attended a four-week interdisciplinary chronic pain management program completed several pain-related measures at admission and discharge, including the PSEQ, Tampa Scale for Kinesiophobia (TSK-11), Pain Catastrophizing Scale (PCS), and Pain Disability Index (PDI). These measures were used to examine the discriminant validity of the PSEQ after dividing the PSEQ scores into three categories (low, medium, and high) based on the standard deviation. Results: The PSEQ at admission was significantly and negatively associated with the TSK, PDI, and PCS admission and discharge scores. The PSEQ cut-offs significantly interacted with the PSEQ and TSK scores at admission and discharge. However, the PSEQ cut-offs did not interact with the PCS or PDI. Discussion: Findings support the use of PSEQ cut-offs when considering the PSEQ and the TSK, with a specific focus on Veterans. Replication of this study with larger samples and functional/occupational measures is recommended. The study also highlights that Veterans may have specific needs and challenges when dealing with chronic pain and this population should be considered in developing and implementing pain management strategies regarding their self-efficacy.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".