Risk-taking behaviour in adolescents. ‘Chance only favors the prepared mind’
Bibliographic record
Abstract
The systematic review by Busse et al 1 draws attention to the prevalence and associated harm of engagement in self-asphyxial behaviours (SAB) (‘choking game’) in young people. SAB have evolved from the largely benign playground ‘games’ based on inducing fainting familiar a generation ago, to become a form of social or learned behaviour with significant risk of fatality because a subgroup of participants engage in solitary and even competitive practices involving strangulation.2 The potential for death or serious injury exists in part because most children and youth do not associate SAB with risk of injury or any long-term harm in spite of widespread awareness of the practice and significant prevalence of engagement, but also because self-asphyxial activities are not ‘on the radar’ of those in a position to counsel and guide in the context of risk-taking behaviours in general. Consequently, to some, the research summarised in this review will provide new insight on the frequency of participation, widespread distribution and potential for adverse outcome from participation in this risk-taking behaviour. It is evident that most parents, healthcare professionals and teachers are unaware that 36%–91% of school-age children are reported to know about SAB. The mean lifetime prevalence of engagement is 7.4% and that fatalities from SAB have been documented in 10 …
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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 | 0.006 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".