Derivation of some contemporary scales to measure adolescent risk-taking in Canada
Bibliographic record
Abstract
OBJECTIVES: To derive a contemporary series of composite indicators of adolescent risk-taking, inspired by the US CDC Framework and Problem Behaviour Theory. METHODS: Factor analyses were performed on 28-risk behaviours in a nationally representative sample of 30,096 Grades 6-10 students from the 2014 Canadian Health Behaviour in School-aged Children study. RESULTS: Three composite indicators emerged from our analysis: (1) Overt Risk-Taking (i.e., substance use, caffeinated energy drink consumption, fighting, and risky sexual behaviour), (2) Aversion to a Healthy Lifestyle (i.e., physical inactivity and low fruit and vegetable consumption), and (3) Screen Time Syndrome (i.e., abnormally high screen time use combined with unhealthy snacking). These three composite indicators of risk-taking were observed consistently with strong psychometric properties across different grade groups (6-8, 9-10). CONCLUSIONS: The three composite indicators of adolescent risk-taking each draw from multiple domains within the CDC framework, and support a novel, empirically directed approach of conceptualizing multiple risk behaviours among adolescents. The measures also highlight the breadth and diversity of risk behaviour engagement among Canadian adolescents. Research and preventive interventions should simultaneously consider the related behaviours within each of these composite indicators.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".