What Factors Do Clinicians, Coaches, and Athletes Perceive Are Associated With Recovery From Low Back Pain in Elite Athletes? A Concept Mapping Study
Bibliographic record
Abstract
OBJECTIVE: Identify factors that elite sport clinicians, coaches, and athletes perceive are associated with low back pain (LBP) recovery. DESIGN: Concept mapping methodology. METHOD: Participants brainstormed, sorted (thematically), and rated (5-point Likert scales: importance and feasibility) statements in response to the prompt, “What factors are associated with the recovery of an elite athlete from low back pain?” Data cleaning, analysis (multidimensional scaling, hierarchical cluster analysis, and descriptive statistics), and visual representation (cluster map and Go-Zone graph) were conducted following concept mapping guidelines. RESULTS: Participants (brainstorming, n = 56; sorting, n = 34; and rating, n = 33) comprised 75% clinicians, 15% coaches, and 10% athletes and represented 13 countries and 17 sports. Eighty-two unique and relevant statements were brainstormed. Sorting resulted in 6 LBP recovery–related themes: (1) coach and clinician relationships, (2) inter-disciplinary team factors, (3) athlete psychological factors, (4) athlete rehabilitation journey, (5) athlete non-modifiable risk factors, and (6) athlete physical factors. Participants rated important recovery factors as follows: athlete empowerment and psychology, coach-athlete and athlete-clinician relationships, care team communication, return-to-sport planning, and identifying red flags. CONCLUSION: Factors perceived as important to LBP recovery in elite athletes align with the biopsychosocial model of community LBP management. Clinicians should consider that an athlete’s psychology, relationships, care team communication, and rehabilitation plan may be as important to their LBP recovery as the formulation of a diagnosis or the medications or exercises prescribed. J Orthop Sports Phys Ther 2023;53(10):610-625. Epub 10 August 2023. doi:10.2519/jospt.2023.11982
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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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".