Prescription opioid use among Australian police detainees
Bibliographic record
Abstract
Prescription opioid diversion and use for non-medical purposes is a growing problem linked to crime and addiction. While offenders are more likely than the general Australian population to use prescription drugs for non-medical purposes, relatively little is known about the types of prescription opioids they use and their patterns of use. Identifying the extent and nature of prescription opioid use among police detainees may assist law enforcement agencies and healthcare providers to allocate resources more effectively. This bulletin draws on data from the Drug Use Monitoring in Australia (DUMA) program collected in January and February 2016. One-quarter of detainees reported prescription opioid use in the last 12 months and almost a fifth engaged in non-medical use of these drugs. The most commonly reported opioid was buprenorphine, and opioids were most commonly obtained from a family member or friend or purchased from a street dealer. About four in 10 users had used more than one type of prescription opioid in the past 12 months, and most had also used other illicit drugs.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".