Celebrating Ten Years of ECHO Ontario Chronic Pain and Opioid Stewardship
Bibliographic record
Abstract
Context Chronic pain is a common, complex, and costly condition that is managed primarily in primary care in Canada. Extension for Community Healthcare Outcomes (ECHO) is a health professions education model that uses telehealth technology to bridge specialists to community clinicians to disseminate best practices and foster interprofessional collaboration. In 2014, amidst a national opioid crisis and debate surrounding opioid guidelines, ECHO Ontario Chronic Pain and Opioid Stewardship (‘ECHO Pain’), the first ECHO in Canada, was launched. Objective To describe the achievements of ECHO Pain and highlight our research and program evaluation progress over ten years. Intervention ECHO Pain started in June 2014 and offered weekly 90-minute sessions that include a didactic lecture followed by a de-identified patient case presentation. The goal of ECHO Pain is to educate, support, and improve chronic pain and opioid management in Ontario’s rural, remote, and underserved areas. Outcome Measures ECHO Pain employed a multi-method approach to evaluation, including pre-post questionnaires and focus group discussions. In this study, we present a narrative summary of our program achievements and research in the past ten years. Results Since 2014, ECHO Pain has completed 20 cycles for a total of 419 sessions, including 924 participants, 22,600+ hours of Continuing Professional Development (CPD) credits, and 573 case presentations. Over ten years, we have received funding from a variety of sources for program planning, implementation, dissemination, and evaluation. Top three takeaways from our research: 1) ECHO Pain changes clinical behaviour – quantitative and qualitative data show that clinicians’ confidence and knowledge related to pain and opioid management increases and that ECHO Pain fosters a strong community of practice. 2) ECHO Pain attracts those who need it the most – high prescribing physicians not only self-select to attend but prescribe less opioids than peers after attending ECHO. 3) ECHO Pain is equitable – through use of telehealth technology, ECHO Pain provides timely education to clinicians practicing in rural, remote, and underserved communities. Conclusion ECHO Pain is a robust health professions education model, with impacts provincially and nationally in terms of spread and scale. Over the last ten years, our research group has demonstrated impact on clinicians’ knowledge, self-efficacy, competence, and opioid prescribing behaviours.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".