Reliability and Validity of the Cross-Culturally Adapted Thai Version of the Tampa Scale for Kinesiophobia in Knee Osteoarthritis Patients
Bibliographic record
Abstract
Purpose: The aim of this study was to develop a cross-culturally adapted Thai version of the Tampa Scale for Kinesiophobia (TSK) and investigate its reliability and validity among patients with knee osteoarthritis.Methods: The TSK was translated into Thai language and culturally adapted in line with the international standards.The Thai TSK questionnaire was then tested for internal consistency, test-retest reliability, and convergent validity by comparing it with the visual analogue scale, Western Ontario and McMaster Universities Osteoarthritis Index, State-Trait Anxiety Inventory, and Timed Up and Go Test.Results: Eighty patients with knee osteoarthritis were included in the study.The Thai version of the TSK was easily comprehended and completed within 6 minutes.The questionnaire showed a good internal consistency (α = 0.90) and high test-retest reliability {ICC (2,1) = 0.934}.Convergent validity showed high correlations with the visual analogue scale, Western Ontario and McMaster Universities Osteoarthritis Index, and State-Trait Anxiety Inventory (r = 0.741, 0.856, and 0.817, respectively).However, there was no significant correlation between the Thai version of the TSK scores and the Timed Up and Go Test results. Conclusion:The Thai version of the TSK has satisfactory reliability and validity for the evaluation of pain-related fear of movement/(re)injury in patients with knee osteoarthritis.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".