Development and validation of the Treatment Expectations in Chronic Pain Scale
Bibliographic record
Abstract
Objectives To develop and examine the psychometric properties of the Treatment Expectations in Chronic Pain (TEC) scale, a brief measure of treatment expectations of chronic non‐cancer pain treatment. Design A cross‐sectional study design was used. Methods After conducting a literature review and expert discussions, a preliminary version of the TEC scale was developed. Cognitive interviews with 10 clinicians and 14 patients were conducted to examine the scale's face validity and item wording. Last, two hundred and five patients on the waitlist for a multidisciplinary pain treatment centre completed a battery of self‐report questionnaires to examine the TEC scale's reliability and construct validity. Mokken scale analysis was conducted to select the final items. Reliability (Cronbach's alpha and Guttman's lambda 2 ) and construct validity (Pearson correlations) were assessed. Results The final scale was composed of nine items that each measured ideal and predicted expectations about process and outcome of treatment. Mokken scale analysis showed the presence of two subscales: ideal and predicted expectations. The TEC scale had good internal consistency ( α = 0.876–0.869) and adequate discriminant validity as assessed by its low correlation with measures of depression, anxiety, and quality of life ( r = −.038 to .114). The scale had however low correlation with a theoretically related measure of optimism ( r = .240). Conclusion The TEC scale is a reliable scale measuring pain treatment expectation. Further evaluation of its psychometric properties is needed. The scale has the potential to deepen our understanding of the role treatment expectations play in chronic non‐cancer pain treatment response. Statement of contribution What is already known on this subject? Expectations play a role in pain perception and the response to pain treatment Patients' expectations about pain and its management are associated with treatment satisfaction The absence of a validated tool to measure treatment expectations in chronic non‐cancer pain prevents further exploration and understanding of the role of expectations in the context of multidisciplinary pain treatment . What does this study add? A new, reliable 9‐item scale measuring treatment expectations among chronic non‐cancer pain patients attending specialized multidisciplinary pain clinics .
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".