Turkish cross-cultural adaptation, construct validity, and reliability of the Treatment Expectations in Chronic Pain Scale
Bibliographic record
Abstract
BACKGROUND: Measuring treatment expectations using the Treatment Expectations in Chronic Pain (TEC) scale has the potential to help clinicians and researchers better understand the role that treatment expectations play within the framework of multimodal pain management settings. OBJECTIVE: The purpose of this study is to determine the cross-cultural adaptation, construct validity and reliability of the TEC Scale in the Turkish language. METHODS: The study included 191 volunteers aged 22–65 with chronic musculoskeletal diseases. This study composed of a six-stage cross-cultural adaptation process, which included translation, translation synthesis, back-translation, expert committee review, pre-testing and documentation submission. The Positivity Scale and Illness Cognition Questionnaire were used to measure convergent validity while the Hospital Anxiety and Depression Scale was used to test divergent validity. The psychometric properties of the Turkish version of the TEC scale was examined by confirmatory factor analysis (CFA). Scale’s internal consistency was examined using Cronbach’s alpha. Pearson correlation coefficients were utilized to evaluate both convergent and divergent validity. The significance level was set at p < .05. RESULTS: The results of the CFA showed that factor structure of predicted subscale fitted well the data (x2/df = 3,07;CFI = 0,91,IFI = 0,91 TLI = 0,87,RMSEA = 0,10). The results of the CFA indicated that factor structure of ideal subscale fitted well with the data (x2/df = 2,38;CFI = 0,92,IFI = 0,93,TLI = 0,90,RMSEA = 0,08). Both subscales of the TEC were strongly correlated. The predicted subscale had moderate relationships to depression, anxiety, and positivity ( r = -0.37 to r = 0.55) but poor correlations with measures of acceptance, perceived benefits and helplessness ( r = -0.24 to 0.35). The ideal subscale had moderate correlations with measures of positivity ( r = 0.36) and depression ( r = -0.38) but poor correlations with measures of acceptance, perceived benefits helplessness and anxiety ( r = 0.14). CONCLUSIONS: The Turkish version of the TEC scale is acceptable, valid, and reliable for use in Turkish patients with chronic musculoskeletal pain in physiotherapy outpatient practice.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".