Asphyxial games or “the choking game”: a potentially fatal risk behaviour
Bibliographic record
Abstract
OBJECTIVES: To determine the prevalence of knowledge about and participation in asphyxial games, sometimes called "the choking game", and how best to raise awareness of this risk-taking behaviour and provide preventive education. DESIGN: Questionnaire; collaborative research model; lay advocacy group/university researchers. SETTING: 8 middle and high schools in Texas (six) and Ontario (two). A recent death from playing the choking game had occurred in one Texas school, and two other fatalities had occurred within the state. SUBJECTS: Students in grades 4-12, aged 9-18 years. INTERVENTION: None. OUTCOME MEASURES: None. RESULTS: Of 2762 surveys distributed, 2504 (90.7%) were completed. The mean (SD) age of the responders was 13.7 (2.2) years. 68% of children had heard about the game, 45% knew somebody who played it, and 6.6% had tried it, 93.9% of those with someone else. Forty percent of children perceived no risk. Information that playing the game could result in death or brain damage was reported as most likely to influence behaviour. The most respected source of a preventive education message was parents for pre-adolescents (43%) or victim/victim's family (36%) for older adolescents. CONCLUSIONS: Knowledge of and participation in self-asphyxial behaviour is not unusual among schoolchildren. The age of the child probably determines the best source (parents or victim/victim's family) of preventive education.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".