Evaluating an addiction medicine unit in Sudbury, Ontario Canada: a mixed-methods study protocol
Bibliographic record
Abstract
BACKGROUND: In response to the escalating global prevalence of substance use and the specific challenges faced in Northern Ontario, Canada, an Addiction Medicine Unit (AMU) was established at Health Sciences North (HSN) in Sudbury. This protocol outlines the approach for a comprehensive evaluation of the AMU, with the aim of assessing its impact on patient outcomes, healthcare utilization, and staff perspectives. METHODS: We conducted a parallel mixed-method study that encompassed the analysis of single-center-level administrative health data and primary data collection, including a longitudinal observational study (target n = 1,200), pre- and post-admission quantitative interviews (target n = 100), and qualitative interviews (target n = 25 patients and n = 15 staff). We implemented a participatory approach to this evaluation, collaborating with individuals who possess lived or living expertise in drug use, frontline staff, and decision-makers across the hospital. Data analysis methods encompass a range of statistical techniques, including logistic regression models, Cox proportional hazards models, Kaplan-Meier curves, Generalized Estimating Equations (GEE), and thematic qualitative analysis, ensuring a robust evaluation of patient outcomes and healthcare utilization. DISCUSSION: This protocol serves as the foundation for a comprehensive assessment designed to provide insights into the AMU's effectiveness in addressing substance use-related challenges, reducing healthcare disparities, and improving patient outcomes. All study procedures have been meticulously designed to align with the ethical principles outlined in the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans. The findings will be disseminated progressively through committees and working groups established for this research, and subsequently published in peer-reviewed journals. Anticipated outcomes include informing evidence-based healthcare decision-making and driving improvements in addiction treatment practices within healthcare settings.
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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 | 0.075 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".