Applying Sport Psychology to Clinical Practice in Athletic Training and Athletic Therapy
Bibliographic record
Abstract
Applying sport psychology evidence and expertise contributes to the effectiveness of clinical practice in the professions of athletic training and athletic therapy (ATAT). Experts and scholars from athletic training, athletic therapy, and sport psychology research and consulting contributed examples of evidence-based practice (EBP) applications around nine broad areas of clinical practice in ATAT: creating caring climates, demonstrating cultural competence, educating patients and professionals, collaborating with the healthcare team, motivating patients for rehabilitation adherence, integrating mental skills, assessing patient-reported outcomes, referring patients for mental health concerns, and making clinical management decisions like return-to-play (RTP). Applying these concepts strengthens clinical practice effectiveness and benefits the many and diverse patient populations served by athletic trainers and athletic therapists (ATs).
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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 | 0.087 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.008 |
| 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; 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".