A Standardized, Evidence-Based Massage Therapy Program for Decentralized Elite Paracyclists: Creating the Model
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Evidence suggests that para-athletes are injured more often than able-bodied athletes. The benefits of massage therapy for these disabled athletes are yet to be explored. This paper documents the process followed for creating a massage program for elite paracycling athletes with the goal to assess effects on recovery, rest, performance, and quality of life both on and off the bike. SETTING: Massage therapists' private practices throughout the United States. PARTICIPANTS: A United States Paracycling team consisting of 9 elite athletes: 2 spinal cord injury, 2 lower limb amputation, 1 upper limb amputation, 1 transverse myelitis, 1 stroke, 1 traumatic brain injury, and 1 visually impaired. DESIGN: The process used to develop a massage therapy program for para-cyclists included meetings with athletes, coaching staff, team exercise physiologist, and sports massage therapists; peer-reviewed literature was also consulted to address specific health conditions of para-athletes. RESULTS: Team leadership and athletes identified needs for quicker recovery, better rest, and improved performance in elite paracyclists. This information was used to generate a conceptual model for massage protocols, and led to creation of the intake and exit questionnaires to assess patient health status and recovery. Forms also were created for a general health intake, therapist information, and a therapist's SOAAP notes. DISCUSSION: The conceptual model and questionnaires developed herein will help to operationalize an exploratory study investigating the feasibility of implementing a standardized massage therapy program for a decentralized elite paracycling team.
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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 | 0.026 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".