A Population-Based Study of the Prevalence and Correlates of Self-Harm in Juvenile Detention
Bibliographic record
Abstract
BACKGROUND: Suicide is the number one cause of death among incarcerated youth. We examined the demographic and forensic risk factors for self-harm in youth in juvenile detention using a Canadian provincial correctional database. METHOD: We analyzed data from de-identified youth aged 12 to 18 at the time of their offense who were in custody in a Manitoba youth correctional facility between January 1, 2005 and December 30, 2010 (N = 5,102). Univariate and multivariate logistic regression analyses determined the association between staff-identified self-harm events in custody and demographic and custodial variables. Time to the event was examined based on the admission date and date of event. RESULTS: Demographic variables associated with self-harm included female sex, lower educational achievement, older age, and child welfare involvement. Custodial variables associated with self-harm included higher criminal severity profiles, younger age at first incarceration, longer sentence length, disruptive institutional behavior, and a history of attempting escape. Youth identified at entry as being at risk for suicide were more likely to self-harm. Events tended to occur earlier in the custodial admission. INTERPRETATION: Self-harm events tended to occur within the first 3 months of an admission stay. Youth with more serious offenses and disruptive behaviors were more likely to self-harm. Individuals with problematic custodial profiles were more likely to self-harm. Suicide screening identified youth at risk for self-harm. Strategies to identify and help youth at risk are needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".