Perceptions of Physical Therapy and The Role of Physical Therapists In Injury Prevention Among Professional Basketball Players: A Qualitative Study
Bibliographic record
Abstract
Background: Injury prevention is critical in competitive professional sports, however, the role of physical therapists in this aspect of healthcare is not fully understood. Purpose: The purpose of this study was to describe professional basketball players' perceptions of physical therapy (PT) and physical therapists' role in injury prevention. Study Design: Qualitative, semi-structured interview. Methods: Thirty-five professional basketball players (mean age 23.1 years ± 3.9; 42% female; 72% African American; 90% college graduates) from over 20 teams participated. Athletes participated in semi-structured interviews that focused on injury prevention and utilization of PT services. Two researchers coded the transcripts, organized the findings into general categories, and created major themes. Data saturation was reached when no new information emerged. Results: Over half (62.9%) stated that PT mainly addressed post-injury and return-to-sport rehabilitation. An overwhelming majority of players highlighted the use of an athletic trainer (AT) over physical therapists in injury prevention due to perceived expertise and trust. Conclusion: While PTs are educated in preventive care and acute injury management, professional basketball players viewed their role primarily for return-to-sport rehabilitation. The organizational structure of healthcare in professional basketball may promote closer professional relationships with ATs while limiting those with physical therapists. The result is that elite athletes may miss out on treatment specific to the PT profession. Level of Evidence: Level 4.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".