Return-to-Play With R2Play: Protocol for Evaluating Cross-Site Feasibility, Face Validity, and Content Validity of a Multidomain Concussion Assessment Tool for Youth
Bibliographic record
Abstract
CONTEXT: Clinical concussion assessments do not typically simulate the speed or complexity of sport. Performance changes arising from combined physical, cognitive, and sensory demands of sport may thus remain undetected during rehabilitation. We developed R2Play, a multidomain return-to-play assessment tool for youth with concussions. R2Play involves levels and conditions that vary in physical, cognitive, and sensory load to simulate the multidomain demands of sport. OBJECTIVES: To explore cross-site feasibility, face validity, and content validity of R2Play by integrating quantitative and qualitative data. METHODS: Convergent mixed-methods feasibility study. Five sites will each recruit 5 clinicians (total nc = 25) and 10 youth sport participants (ages 10-25 y) with a history of concussion in the previous year (total ny = 50). Feasibility will be evaluated using quantitative criteria for acceptability, demand, implementation, practicality, and integration, and qualitative investigated data from content analysis of postassessment interviews with youth and clinician participants. Face validity will be investigated in postassessment interviews. Content validity will be established through (1) changes in performance metrics (time to completion, errors, and heart rate) across R2Play levels, (2) youth-perceived physical and cognitive exertion for each level, and (3) overall clinician perceptions determined through postassessment interviews. Qualitative and quantitative data will be merged through joint display to identify areas of convergence, divergence, and complementarity, and to establish meta-inferences about feasibility, face validity, and content validity. DISCUSSION: This study aims to demonstrate the face and content validity of R2Play, and its feasibility for cross-site implementation. Findings will guide further iteration of R2Play and establish the foundation for a larger multicenter validation study to establish the psychometric properties of R2Play. This work represents an important first step toward the implementation of an ecologically valid multidomain assessment tool designed to support a safe and efficient return-to-play after concussion, ultimately reducing the risk of recurrent concussion and subsequent injury.
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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 | 0.073 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.015 |
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".