Fear and Avoidance of Movement in People with Chronic Pain: Psychometric Properties of the 11-Item Tampa Scale for Kinesiophobia (TSK-11)
Bibliographic record
Abstract
PURPOSE: To determine the psychometric properties of the 11-item Tampa Scale for Kinesiophobia (TSK-11) in patients with heterogeneous chronic pain. METHODS: The study evaluated test-retest reliability (intra-class correlation coefficient), cross-sectional convergent construct validity (Pearson product-moment correlation between TSK-11 and the Pain Catastrophizing Scale [PCS] scores at admission), and sensitivity to change of the TSK-11 (area under the receiver operating characteristic [ROC] curve) in patients (n=74) with heterogeneous chronic pain. We used two data sets (retrospective, n=56; prospective, n=18). All patients attended the 4-week interdisciplinary chronic pain management programme at Chedoke Hospital, Hamilton Health Sciences, Hamilton, Ontario. RESULTS: The test-retest reliability of the TSK-11 was 0.81 (95% CI, 0.58-0.93), the standard error of measurement was 2.41 (90% CI, 1.47-2.49), and the minimal detectible change score was 5.6. The correlation between TSK-11 and PCS at admission was 0.60 (95% CI, 0.43-0.73). The area under the ROC curve was 0.73 (95% CI, 0.57-0.88). CONCLUSIONS: The study results provide evidence for the test-retest reliability, cross-sectional convergent construct validity, and sensitivity to change of the TSK-11 in a population with heterogeneous chronic pain.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".