The Tampa Scale of Kinesiophobia
Bibliographic record
Abstract
OBJECTIVE: The aims of this systematic review were to identify the different versions of the Tampa Scale of kinesiophobia (TSK) and to report on the psychometric evidence relating to these different versions for people experiencing musculoskeletal pain. METHODS: Medline [Ovid] CINAHL and Embase databases were searched for publications reporting on the psychometric properties of the TSK in populations with musculoskeletal pain. Risks of bias were evaluated using the COSMIN risk of the bias assessment tool. RESULTS: Forty-one studies were included, mainly with a low risk of bias. Five versions of the TSK were identified: TSK-17, TSK-13, TSK-11, TSK-4, and TSK-TMD (for temporomandibular disorders). Most TSK versions showed good to excellent test-retest reliability (intraclass coefficient correlation 0.77 to 0.99) and good internal consistency (ɑ=0.68 to 0.91), except for the TSK-4 as its reliability has yet to be defined. The minimal detectable change was lower for the TSK-17 (11% to 13% of total score) and the TSK-13 (8% of total score) compared with the TSK-11 (16% of total score). Most TSK versions showed good construct validity, although TSK-11 validity was inconsistent between studies. Finally, the TSK-17, -13, and -11 were highly responsive to change, while responsiveness has yet to be defined for the TSK-4 and TSK-TMD. DISCUSSION: Clinical guidelines now recommend that clinicians identify the presence of kinesiophobia among patients as it may contribute to persistent pain and disability. The TSK is a self-report questionnaire widely used, but 5 different versions exist. Based on these results, the use of TSK-13 and TSK-17 is encouraged as they are valid, reliable, and responsive.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".