The Tampa Scale of Kinesiophobia: Structural Validation among Adolescents with Idiopathic Scoliosis Undergoing Spinal Fusion Surgery
Bibliographic record
Abstract
Aims Spinal fusion surgery is one of the most invasive orthopedic surgeries. Pain while moving or a fear of experiencing pain after surgery may delay return to function and cause prolonged disability. The purpose of the study was to examine the psychometric properties of the Tampa Scale of Kinesiophobia (TSK) in pediatric patients undergoing scoliosis surgery.Methods Fifty-five adolescents (10–18 years old) scheduled for spinal fusion surgery were enrolled. Participants completed the TSK questionnaire before surgery and six weeks after surgery. Reliability, exploratory and confirmatory factor analyses were performed on the two-factors TSK including activity avoidance (TSK-AA) and somatic focus (TSK-SF).Results Before and after surgery, all TSK-AA items conformed into the same factor component and revealed good internal reliability with Cronbach’s alpha of .76 and .70 respectively. TSK-SF items were separated into different factor components and revealed poor reliability (.11 and .56). The TSK-AA also produced an adequate fit to the data, as reflected with several fit indices at both timepoints, respectively: χ2/df = 1.19 and 1.22; CFI=.96 and .94; and RMSEA=.06 and .06.Conclusions The TSK-AA demonstrated good psychometric properties in patients undergoing scoliosis surgery, which provides empirical evidence for pediatrics. Its validation in distinct populations and settings is recommended prior to its use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| 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 | 0.000 | 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".