Road to Recovery: protocol for a mixed-methods prospective cohort study evaluating the impact of a new model of substance use care in a Canadian setting
Bibliographic record
Abstract
INTRODUCTION: The Road to Recovery (R2R) Initiative is an innovative model of substance use care that seeks to increase treatment capacity by creating approximately 100 new addiction treatment beds to provide on-demand addiction care in Vancouver, British Columbia, for patients with substance use disorders. The new model also coordinates the region's existing clinical substance use services to support patients across a care continuum that includes traditional office-based addiction treatment and harm reduction services, early withdrawal management and more intensive abstinence-based treatment programming. To understand the impact of offering on-demand and coordinated substance use care, an observational cohort of individuals who access any R2R clinical service will be created to examine health and social outcomes over time. METHODS AND ANALYSIS: This prospective mixed-methods study will invite individuals from Vancouver, Canada, who access substance use treatment through the R2R model of care to (1) complete a baseline and 12-month follow-up quantitative questionnaire that solicits sociodemographic, substance use and previous addiction treatment data and (2) provide consent to the use of participants' personal identifiers to access health records for chart review and for annual linkage to select health and administrative databases to allow for ongoing (virtual) community follow-up over 5 years. Additionally, a purposive sample of cohort participants will be invited to participate in baseline and 12-month follow-up qualitative interviews to share their experiences accessing R2R and identify challenges and opportunities associated with the implementation of R2R. ETHICS AND DISSEMINATION: The study was approved by the University of British Columbia Providence Health Care Research Ethics Board in September 2023. Results from the proposed study will be published in peer-reviewed journals, presented at national and international scientific conferences and disseminated through regular meetings with policymakers, individuals with lived and living experience, and other high-level stakeholders, academic presentations and lay media.
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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 | 0.081 | 0.050 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.074 | 0.011 |
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".