Multi-method evaluation of a physician-led pilot addiction consult service
Bibliographic record
Abstract
INTRODUCTION: Addiction consultation services are hospital-based specialist programs designed to support the care of patients with substance use disorders (SUDs). This study aimed to: (1) describe service volumes and patient demographics for a pilot addiction consultation program, (2) compare clinical outcomes between patients seen prior to and after program implementation, and (3) explore provider perceptions, referral patterns, and clinical practice. METHODS: Using the RE-AIM framework, we conducted a formative multi-method evaluation at a tertiary care hospital. Quantitative data about the uptake of the pilot program and clinical descriptions of three patient groups (consult patients, pre-program baseline patients, post-program non-consult patients) was collected through chart review. A provider survey with closed and open-ended questions was used to explore provider practice patterns, perceived needs, roles, and challenges. RESULTS: Most consult requests were from General Internal Medicine (136/181, 75.1 %). Consult orders were usually placed during service hours (169/181 93.4 %), with a median time between admission and consult request of 1 day (IQR 0-2). Consultation was linked to higher odds of receiving a pharmacotherapy prescription compared to baseline (OR 5.82 [95 % CI 3.05-11.99], p < 0.001) and patients not receiving consultation (OR 6.78 [95 % CI 2.76-20.75], p < 0.001). Survey findings highlighted non-addiction specialist providers' lack of confidence with substance use pharmacotherapy and consultation for counselling, resource navigation, and harm reduction. CONCLUSIONS: The consult program demonstrated consistent uptake and was associated with increased access to pharmacotherapy for hospitalized patients, supporting improved inpatient addiction care. Non-addiction providers identified value in the consult program beyond pharmacotherapy and identified challenges with this patient population.
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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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".