Risk of Injury to Others: The Development of an Algorithm to Identify Children and Youth at High-Risk of Aggressive Behaviours
Bibliographic record
Abstract
Youth violence is considered one of the most preventable causes of morbidity and premature mortality. Various risk factors have previously been identified, however, there is presently a crucial need to develop effective decision-support tools in order to identify children and youth at increased risk for violence. The current study utilised data collected from the interRAI Child and Youth Mental Health Screener (ChYMH-S), within the province of Ontario, to develop and validate a methodology for the purpose of identifying young persons who were at greater risk of harm to others. Additional data from 59 mental health agencies validated the algorithm, and it was found to be a strong predictor of harmful behaviour toward others. The RIO algorithm provides a valuable decision-support tool with strong psychometric properties that may be used to identify young persons who exhibit signs or symptoms associated with increased likelihood of harm toward others, in order to provide early intervention efforts for these vulnerable youth, thereby reducing the likelihood of future aggressive behaviours.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".