Fear of movement and (re)injury in chronic musculoskeletal pain: Evidence for an invariant two-factor model of the Tampa Scale for Kinesiophobia across pain diagnoses and Dutch, Swedish, and Canadian samples
Bibliographic record
Abstract
The aims of the current study were twofold. First, the factor structure, reliability (i.e., internal consistency), and validity (i.e., concurrent criterion validity) of the Tampa Scale for Kinesiophobia (TSK), a measure of fear of movement and (re)injury, were investigated in a Dutch sample of patients with work-related upper extremity disorders (study 1). More specifically, examination of the factor structure involved a test of three competitive models: the one-factor model of all 17 TSK items, a one-factor model of the TSK (Woby SR, Roach NK, Urmston M, Watson P. Psychometric properties of the TSK-11: a shortened version of the Tampa Scale for Kinesiophobia. Pain 2005;117:137-44.), and a two-factor model of the TSK-11. Second, invariance of the aforementioned TSK models was examined in patients with chronic musculoskeletal pain conditions (i.e., work-related upper extremity disorders, chronic low back pain, fibromyalgia, osteoarthritis) from The Netherlands, Sweden, and Canada was assessed (study 2). Results from study 1 showed that the two-factor model of the TSK-11 consisting of 'somatic focus' (TSK-SF) and 'activity avoidance' (TSK-AA) had the best fit. The TSK factors showed reasonable internal consistency, and were modestly but significantly related to disability, supporting the concurrent criterion validity of the TSK scales. Results from study 2 showed that the two-factor model of the TSK-11 was invariant across pain diagnoses and Dutch, Swedish, and Canadian samples. Altogether, we consider the TSK-11 and its two subscales a psychometrically sound instrument of fear of movement and (re)injury and recommend to use this measure in future research as well as in clinical settings.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".