The Incidence and Prediction of Self-Injury among Sentenced Prisoners
Bibliographic record
Abstract
OBJECTIVE: Prevention of self-injurious behaviour is an important priority in correctional settings given higher rates among inmates. Our study estimated the reported incidence of self-injury during the first 180 days in prison and tested potential risk and protective factors using official prison records. METHODS: We conducted a retrospective cohort study using secondary data for 5154 admissions to the Correctional Service of Canada during 2011. Relative risks were estimated with Poisson regression. Recursive partitioning was used to create a parsimonious model of characteristics of offenders who engage in self-injury. RESULTS: Thirty-six of 5154 (0.7%) offenders engaged in 1 or more incidents of self-injury during their first 180 days of incarceration. Educational and occupational achievement, family history, demographic factors, mental health service use, and results of mental health screening at intake were predictive of self-injury. Recursive partitioning models identified about 23% of inmates who presented with multiple risk factors, and had increased incidence of self-injury. A comparison of a model using information at intake to a model also incorporating events in prison suggested that events in prison added little to the detection of self-injury. CONCLUSIONS: Given high rates of most risk factors, screening for self-injury during early incarceration will be overinclusive. However, it may identify a group of inmates with complex needs for whom interdisciplinary responses are needed to address wide-ranging social, family, behavioural, and mental health deficits.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".