ESport programs in high school: what’s at play?
Bibliographic record
Abstract
Background: A growing number of high schools in Canada offer eSports (ES) in their facilities, which raises concerns regarding this activity's potential health risks for adolescents. Methods: The aim of this study is to describe the characteristics of 67 adolescent ES players (ESp) and to compare them to 109 recreational gamers in their high school (nESp). The two groups were compared on (1) sociodemographic and academic characteristics; (2) online and offline activities; (3) psychological characteristics. Results: Results show that ESp spend more time on online activities and report a higher proportion of problematic gaming compared to the nESp group. ESp report more often that gaming has positive consequences on their physical health and report more often negative consequences on their education compared to the nESp group. Conclusion: These results underscore the importance of screening gaming problems among adolescent ES players. Targeted prevention should be carried out with these teenagers and in order to be adapted, prevention efforts should consider both, the positive and negative consequences that ESp experience from gaming.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".