Profiling Violent Incidents in a Drug Treatment Sample: A Tripartite Model Approach
Bibliographic record
Abstract
This research focuses on the qualitatively descriptive accounts of drug-related violent incidents drawn from a treatment sample of 571 substance abuse clients in Ontario. Nearly half (n = 269) had experienced at least one violent incident in the past year, and 91% had used one or more substances prior to the most recent episode. The classification of the explicitly drug-related violent events (n = 176), based on Goldstein's tripartite model, is its first application in an adult drug treatment sample. Although respondents were not criminal offenders, and interpersonal violence related to psychopharmacological effects predominated, economic or systemic linkages related to drug scarcity and the drug market were implicated in one fifth of all occurrences. Alcohol and cocaine were the substances most implicated in all three aspects of the model. Since a drug treatment sample is a high-risk group for violence, interventions that raise awareness of potential for violence linked to not only intoxication but also scarcity conflicts and illicit drug market involvement are warranted. Since most violence occurs in the community, such initiatives may benefit those in treatment and serve as an important public health strategy.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".