Profiling Violent Incidents in a Drug Treatment Sample: A Tripartite Model Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it