Trends in treatment of problematic cannabis use in Ontario’s specialized addiction treatment system from 2010/11 to 2015/16: a repeated cross-sectional study of a health administrative database
Bibliographic record
Abstract
BACKGROUND: Little is known about trends in the treatment of problematic cannabis use in Canada. Trends in treatment utilization for problematic cannabis use were examined, as well as trends in the associated sociodemographic characteristics and frequency of cannabis use. METHODS: This was a repeated cross-sectional study using data from the Drug and Alcohol Treatment Information System, capturing utilization of all community funded addiction treatment services in Ontario, Canada. Clients in treatment for their own problematic cannabis use from 2010/11 to 2015/16 were included. Two distinct groups were formed: clients with problematic cannabis use only (the cannabis-only group) and clients with problematic use of cannabis and other substances (the cannabis-plus group). Estimates of the number of clients in each of these groups and their cannabis use frequency (past 30 days) were characterized over time by new admissions and total caseload (new admissions plus carryovers). RESULTS: There were 152 984 admissions for 83 621 clients over the study period. The number of clients with new admissions in the cannabis-only group decreased from 2954 (95% confidence interval [CI] 2848-3062) in 2010/11 to 2342 (95% CI 2248-2439) in 2015/16. Similar downward trends were observed in the number of clients in the total caseload of this group. The number of clients with new admissions in the cannabis-plus group was stable, but the total caseload increased from 20 139 clients (95% CI 19 862-20 419) in 2011/12 to 21 816 (95% CI 21 527-22 107) in 2015/16. Proportions of daily cannabis use increased among clients in both groups. INTERPRETATION: The number of clients in treatment for problematic cannabis use only decreased over the study period, but the frequency of cannabis use increased among clients in both groups. Given the potential reductions in treatment that is unnecessary from a clinical standpoint, alignment of treatment programming with disorder severity may be warranted.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".