R2Play and Stakeholder Needs: Fostering User-driven Technology to Support Return-to-play Decision-making
Bibliographic record
Abstract
Background: Following concussion, return-to-play protocols rely on a battery of single-domain assessments to assess recovery. Yet, single-domain assessments may fail to detect symptoms elicited by the cognitive, physical, and socio-emotional multi-domain demands when an athlete returns to sport.Objective: To bridge this gap by developing the R2Play system, which facilitates the implementation of a multi-domain return-to-play assessment for young athletes. Method: To address this aim, I have: (1) conducted a scoping review of multi-domain assessments; (2) conducted needs-assessment interviews with stakeholders; (3) collaborated with our research team to build a testable prototype; and (4) carried out proof-of-concept testing for R2Play. Results: The current thesis details the development and initial testing of the R2Play system, providing proof-of-concept and outlining next steps based on user feedback. Conclusions: This thesis contributes a prototype of the R2Play system, and highlights the potential of technology in clinical assessment and the benefits of user-centered design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".