An environmental scan of residential treatment service provision in Ontario
Bibliographic record
Abstract
BACKGROUND: Ontario has one of the highest rates of substance-related harms in Canada. Residential treatment programs in the province provide a variety of in-house treatment services to support the needs of individuals with substance use disorders (SUD). However, these programs are not standardized, often varying in the type, quality, and availability of services offered, including evidence-based interventions such as Opioid Agonist Treatment (OAT). Local treatment systems are also rather fragmented and complex to navigate, creating barriers for potential services users to identify and make informed choices on available treatment options. METHODS: Between May to August 2023, we conducted an environmental scan to capture available information on all publicly-funded residential treatment programs in Ontario using the ConnexOntario service portal, a government-funded, health services information platform. Data were captured on organization name, geographical location, program description, program type (residential addictions treatment or supportive recovery programs), eligibility criteria, target population, the program's OAT policies, number of available beds, minimum and maximum length of stay, projected wait times, funding source, and associated fees for program admission. Data were extracted and organized by geographic region, and findings were presented descriptively. RESULTS: A total of 102 residential addiction treatment programs and 36 residential supportive recovery programs in Ontario were identified. The scan noted substantial regional variations in program availability and wait times, along with a lack of programs tailored to unique populations such as women, youth, and Indigenous peoples. There is also a paucity of publicly-available information on program offerings, including detailed specifics on OAT policies within residential treatment programs that are crucial to ensuring that the services being offered are safe and grounded in evidence-based practice. CONCLUSIONS: Findings from the scan highlight notable gaps in program types, offerings, and availability among residential treatment programs in the province, including a lack of standardization on OAT policies across programs. Efforts should be made to ensure access to treatment-specific program information relevant to potential service users and to enhance coordinated access to residential treatment services in the province.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".