Reductions in non-medical prescription opioid use among adults in Ontario, Canada: are recent policy interventions working?
Bibliographic record
Abstract
BACKGROUND: Non-medical prescription opioid use (NMPOU) and prescription opioid (PO) related harms have become major substance use and public health problems in North America, the region with the world's highest PO use levels. In Ontario, Canada's most populous province, NMPOU rates, PO-related treatment admissions and accidental mortality have risen sharply in recent years. A series of recent policy interventions from governmental and non-governmental entities to stem PO-related problems have been implemented since 2010. FINDINGS: We compared the prevalence of NMPOU in the Ontario general adult population (18 years+) in 2010 and 2011 based on data from the 'Centre for Addiction and Mental Health (CAMH) Monitor' (CM), a long-standing annual telephone interview-based representative population survey of substance use and health indicators. While 'any PO use' (in past year) changed non-significantly from 26.6% to 23.9% (Chi2 = 2.511; df = 1; p = 0.113), NMPOU decreased significantly from 7.7% to 4.0% (Chi2 = 14.786; df = 1; p < 0.001) between 2010 and 2011. Over-time changes varied by age group but not by sex. CONCLUSIONS: The observed substantial decrease in NMPOU in the Ontario adult population could be related to recent policy interventions, alongside extensive media reporting, focusing on NMPOU and PO-related harms, and may mean that these interventions have shown initial effects. However, other casual factors could have been involved. Thus, it is necessary to systematically examine whether the observed changes will be sustained, and whether other key PO-related harm indicators (e.g., treatment admissions, accidental mortality) change correspondingly in order to more systematically assess the impact of the policy measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".