Fear of movement/(re)injury in chronic pain: A psychometric assessment of the original English version of the Tampa scale for kinesiophobia (TSK)
Bibliographic record
Abstract
The Tampa scale for kinesiophobia (TSK) was developed to measure fear of movement/(re)injury in chronic pain patients. Although studies of the Dutch adaptation of the TSK have identified fear of movement/(re)injury as an important predictor of chronic pain, pain-related avoidance behaviour, and disability, surprisingly little data on the psychometric properties of the original English version of the TSK are available. The present study examined the reliability, construct validity and factor structure of the TSK in a sample of chronic pain patients (n=200) presenting for an interdisciplinary functional restoration program. Consistent with prior evaluations of the Dutch version of the TSK, the present findings indicate that the English TSK possesses a high degree of internal consistency and is positively associated with related measures of fear-avoidance beliefs, pain catastrophizing, pain-related disability and general negative affect. The TSK was not related to individual differences in physical performance testing as assessed using standardised treadmill and lifting tasks. Confirmatory factor analyses suggest that the TSK is best characterized by a three-factor trait method model that includes all 17 of the original scale items and takes into account the distinction between positively and negatively keyed items. The results of the present study provide important details regarding the psychometric properties of the original English version of the TSK and suggest that it may be unnecessary to remove the negatively keyed items in an attempt to improve scale validity.
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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 | 0.012 |
| 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 | 0.000 | 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".