The Association of Perceived Controllability, Fear, and Catastrophizing With Pain Experience: Insights From a Cross-sectional Study in Individuals With Lateral Elbow Tendinopathy
Bibliographic record
Abstract
OBJECTIVE: To investigate associations between locus of control, fear, and pain experience in individuals with lateral elbow tendinopathy (LET). DESIGN: Cross-sectional observational study using validated patient-reported outcome measures alongside neurophysiological assessments. METHODS: Participants with LET were assessed using the Short-Form McGill Pain Questionnaire, numeric pain-rating scale, and Central Sensitization Inventory to evaluate pain intensity. Pain interference was measured via the Patient-Specific Functional Scale, Hospital Anxiety and Depression Scale, Pain Catastrophizing Scale, and Fear-Avoidance Beliefs Questionnaire. Neurophysiological aspects of pain were evaluated using Pain-Free Grip Strength, Two-Point Discrimination, and Laterality tests. Group characteristics were compared using chi-square and independent-samples t tests. Logistic regression identified significant predictors of chance locus of control, and linear regression analyzed the association of pain intensity with fear-avoidance behaviors. RESULTS: There was a significant association between higher levels of fear-avoidance beliefs, increased pain catastrophizing, and elevated continuous pain intensity. Psychological factors were associated with pain experience in LET. Chance locus of control was not associated with LET pain. CONCLUSION: Psychological constructs, particularly fear-avoidance beliefs and catastrophizing, were strongly associated with pain intensity in LET. These results underscore the importance of addressing psychological factors in clinical management to improve patient outcomes. JOSPT Open 2026;4(1):111-120. Epub 9 December 2025. doi:10.2519/josptopen.2025.0179
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".