Innovations to the ECHO model to enhance reach and network-building among addiction clinicians in Western Canada
Bibliographic record
Abstract
Abstract Background Building capacity for evidence-based treatment and support for people with substance use disorders (SUD) is an urgent priority in the context of the toxic drug poisoning crisis. We implemented the first substance use-focused Project Extension for Community Healthcare Outcomes (ECHO) in Western Canada for health care providers, to enhance their clinical addiction skills and knowledge, facilitate practice change, and foster a supportive community of practice. The aims of this article are to describe our innovations to the Project ECHO model in British Columbia (BC) and Yukon, and present key program outcomes. Methods A pragmatic multi-methods program evaluation employed observational records of BC ECHO on Substance Use session attendance, cross-sectional and longitudinal participant surveys, and qualitative interviews with participants to assess satisfaction, relevance, and preparation to use evidence-based approaches, practice change intentions, and actual behaviours. Results The 52 ECHO sessions (from June 2019 to July 2022) attracted 2134 unique registrants with 5089 attendances (mean 124/session), 2132 newsletter subscribers, and 5842 podcast downloads. The evaluation included 844 post-session survey respondents and 53 interview participants. The program included ECHO sessions with rolling attendance; widely accessed supplemental formats (e.g., newsletter, podcast, clinical tools, archived presentation recordings); variable, regional hub representation; and evidence-based content developed by medical writers. These features contributed to broad geographic and discipline reach, high-quality program content, and high mean session satisfaction ratings (4.2/5). Key qualitative themes emerged, related to knowledge and skill acquisition, gaining confidence in providing SUD care, facilitating shared decision-making, increasing compassion for patients, consolidating learning and applying it to practice, and reducing isolation through expanded networks. Conclusions The ECHO model is an effective way to improve capacity in SUD care for physicians and nurse practitioners, while offering benefits for interprofessional attendees. Our findings can inform innovations in other ECHO programs to enhance reach, engagement, and impact.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| 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".