Attributable fractions for substance use in relation to crime
Bibliographic record
Abstract
AIMS: Building upon an existing methodology and conceptual framework for estimating the association between the use of substances and crime, we calculated attributable fractions that estimate the proportion of crimes explained by alcohol and six other categories of psychoactive substances. DESIGN: Cross-sectional surveys. SETTING: Canadian federal correctional institutions. PARTICIPANTS: Canadian men (n = 27 803) and women (n = 1335) offenders who began serving a custodial sentence in a Canadian federal correctional institution between 2006 and 2016. MEASUREMENTS: Offenders completed the computerized assessment of substance abuse, a self-report tool designed to assess (1) whether the offence for which they were convicted would have occurred had they not been intoxicated from alcohol or another substance, (2) whether they committed the offence to support their alcohol or other substance use and (3) whether they were dependent on alcohol (alcohol dependence scale) or another substance (drug abuse screening test). Offences were grouped into four mutually exclusive categories: violent crimes, non-violent crimes, impaired driving and substance-defined crimes. This study focused on violent and non-violent crime categories. Substances assessed were: alcohol, cannabis, opioids, other central nervous system (CNS) depressants, cocaine, other CNS stimulants and other substances. FINDINGS: According to offender self-report, 42% of all violent and non-violent crime would probably not have occurred if the perpetrator had not been under the influence of, or seeking, alcohol or other substances. Between 2006 and 2016, 20% of violent crimes and 7% of non-violent crimes in Canada were considered attributable to alcohol. In contrast, all other psychoactive substance categories combined were associated with 26% of all violent crime and 25% of non-violent crime during the same time-frame. CONCLUSIONS: Attributable fraction analyses show that more than 42% of Canadian crime resulting in a custodial sentence between 2006 and 2016 would probably not have occurred if the perpetrator had not been under the influence of or seeking alcohol or other drugs. Attributable fractions for alcohol and substance-related crime are a potentially useful resource for estimating the impact of alcohol and other substances on crime.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".