Which Psychological and Psychosocial Constructs Are Important to Measure in Future Tendinopathy Clinical Trials? A Modified International Delphi Study With Expert Clinician/Researchers and People With Tendinopathy
Bibliographic record
Abstract
OBJECTIVE: To identify which psychological and psychosocial constructs to include in a core outcome set to guide future clinical trials in the tendinopathy field. DESIGN: Modified International Delphi study. METHODS: In 3 online Delphi rounds, we presented 35 psychological and psychosocial constructs to an international panel of 38 clinician/researchers and people with tendinopathy. Using a 9-point Likert scale (1 = not important to include, 9 = critical to include), consensus for construct inclusion required ≥70% of respondents rating “ extremely critical to include” (score ≥7) and ≤15% rating “ not important to include” (score ≤3). Consensus for exclusion required ≥70% of respondents rating “ not important to include” (score ≤3) and ≤15% of rating “critical to include” (score ≥7). RESULTS: Thirty-six participants (95% of 38) completed round 1, 90% (n = 34) completed round 2, and 87% (n = 33) completed round 3. Four constructs were deemed important to include as part of a core outcome set: kinesiophobia (82%, median: 8, interquartile range [IQR]: 1.0), pain beliefs (76%, median: −7, IQR: 1.0), pain-related self-efficacy (71%, median: 7, IQR: 2.0), and fear-avoidance beliefs (73%, median: −7, IQR: 1.0). Six constructs were deemed not important to include: perceived injustice (82%), individual attitudes of family members (74%), social isolation and loneliness (73%), job satisfaction (73%), coping (70%), and educational attainment (70%). Clinician/researchers and people with tendinopathy reached consensus that kinesiophobia, pain beliefs, pain self-efficacy, and fear-avoidance beliefs were important psychological constructs to measure in tendinopathy clinical trials. J Orthop Sports Phys Ther 2024;54(1):14-25. Epub 20 September 2023. doi:10.2519/jospt.2023.11903
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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 | 0.324 | 0.288 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".