An Observational Study of a Digital Substance Use and Recovery Program
Bibliographic record
Abstract
OBJECTIVE: Digital substance use treatment programs present an opportunity to provide nonresidential care for people with problematic substance use. In June 2021, the provincial government in Ontario provided free access to Breaking Free Online (BFO), a digital behavioral change program for people with substance use disorders. METHODS: An observational study was conducted with retrospective data to characterize clients' use and engagement patterns in BFO and examine changes in self-reported outcomes. RESULTS: In total, 6,370 individuals registered for BFO between June 2021 and October 2022, of whom 3,650 completed the intake assessment. Most of these clients were self-referred (64%), with 37% having been referred by health service providers. More than one-half of the clients (52%) resided in Ontario West or East regions. Support for addressing problematic alcohol use was the most requested program (40%). By October 2022, about 44% of the clients had completed between one and four of 12 program strategies. Analysis revealed significant changes in pre-post scores across four validated scales (p<0.001), indicating a decrease in anxiety and depression, an increase in quality of life, an improvement in recovery progression, and a decrease in severity of symptoms associated with substance use disorders. CONCLUSIONS: BFO clients with higher completion rates had the most improvement across the scales used; however, clients with lower and medium completion rates also had improvements. Because of the shame and stigma associated with substance use, digital supports with low barriers to entry can help support the autonomy, privacy, and preferences of individuals seeking help for problematic substance use.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".