Patterns of methadone maintenance treatment provision in Ontario: Policy success or pendulum excess?
Bibliographic record
Abstract
OBJECTIVE: To describe recent trends and patterns in methadone maintenance treatment (MMT) practice regionally and over time in the province of Ontario. DESIGN: Population-based descriptive study using health administrative data between September 1, 2011, and December 31, 2014. SETTING: Ontario. PARTICIPANTS: All active MMT-prescribing physicians and patients receiving MMT in the study period. MAIN OUTCOME MEASURES: Characteristics of MMT-prescribing physicians, including age, sex, specialty type, practice region, and practice volume; characteristics of patients receiving MMT, including age, sex, neighbourhood income, and region of residence. RESULTS: Between September 1, 2011, and December 31, 2014, the number of MMT-prescribing physicians and patients who received MMT increased by 26% and 42%, respectively. In 2014, there was a total of 312 MMT-prescribing physicians and 49 703 patients receiving MMT. In 2014 and on a per capita basis, patients receiving MMT were more prevalent in rural regions; and within rural regions, there were disproportionately large numbers of young female MMT patients residing in low-income neighbourhoods. CONCLUSION: The number of physicians prescribing MMT and patients receiving MMT has increased substantially between 2011 and 2014, with the largest per capita distribution occurring in rural regions and involving young adults. While availability of and access to MMT has improved considerably from before 2000 to levels of high use, these developments are likely influenced by recent trends in the proliferation of prescription opioid misuse across general populations.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".