Managing the health of the eSport athlete: an integrated health management model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: eSport is a form of electronic gaming, also known as professional or competitive video gaming, and is growing at a rapid pace worldwide. Over 50 US colleges have established varsity gaming teams over the past three years; some colleges offer eSport scholarships as they do for traditional sports. There is little objective research on the health habits of these players who are often placed under the direction of the athletics department on college campuses, and there is currently no health management model on how to treat these new athletes. METHODS: Anonymous electronic surveys were sent to 65 collegiate eSport players from nine universities across the USA and Canada inquiring about gaming and lifestyle habits, and musculoskeletal complaints due to eSport competition. RESULTS: Players practiced between 3 and 10 hours per day. The most frequently reported complaint was eye fatigue (56%), followed by neck and back pain (42%). eSport athletes reported wrist pain (36%) and hand pain (32%). Forty per cent of participants do not participate in any form of physical exercise. Among the players surveyed, only 2% had sought medical attention. CONCLUSION: eSport players, just like athletes in traditional sports, are susceptible to overuse injuries. The most common complaint was eye fatigue, followed by neck and back pain. This study shows eSport athletes are also prone to wrist and hand pain. This paper proposes a health management model that offers a comprehensive medical team approach to prevent and treat eSport athletes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it