Epidemiology of firesetting in adolescents: mental health and substance use correlates
Bibliographic record
Abstract
OBJECTIVE: Despite high rates of firesetting among community adolescents, little is known about its correlates. This study identifies the mental health and substance use correlates of four firesetting levels in an epidemiological sample of adolescents. METHODS: Three thousand, nine hundred and sixty-five (3,965) students in grades 7 to 12 were surveyed. Multinomial analyses were used to compare non-firesetters; desisters (lifetime, but no past-year firesetting); low frequency firesetters (once or twice in the past 12 months); and high frequency firesetters (3 + times) on measures of mental health and substance use. RESULTS: Twenty-seven percent of youth reported firesetting during the past year. Of these, 13.7% reported one or two episodes, and 13.5% reported 3 or more episodes. Firesetting was more prevalent among males and among those in high school. Youth who began firesetting before age 10 were more likely to report frequent firesetting during the past year. Compared to non-firesetters, the firesetting groups had elevated risk profiles. Desisters and low frequency firesetters were more likely to report psychological distress, binge drinking, frequent cannabis use, and sensation seeking. Low frequency firesetters also reported higher rates of delinquent behavior, suicidal intent, and low parental monitoring than non-firesetters. High frequency firesetters reported elevated risk ratios for all of these risk indicators plus other illicit drug use. The cumulative number of risk indicators was positively associated with firesetting severity. CONCLUSIONS: Firesetting is associated with psychopathology and substance use during adolescence. Findings highlight the need for programs to address the mental health and substance use problems that co-occur with firesetting.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".