Project ECHO Telementoring Intervention for Managing Chronic Pain in Primary Care: Insights from a Qualitative Study
Bibliographic record
Abstract
Objective: Family physicians in Canada receive little training in chronic pain management; concomitantly, they face increasing pressure to reduce their prescribing of opioids. Project ECHO Ontario Chronic Pain/Opioid Stewardship (ECHO) is a telementoring intervention for primary care practitioners that enhances their pain management skills. This qualitative study reports participants' experiences and assessment of ECHO. Design: An opportunistic sample of multidisciplinary primary care providers attending one of three residential weekend workshops participated in focus group discussions. Setting: University or hospital facilities in Toronto, Thunder Bay, and Kingston, Ontario, Canada. Subjects: Seventeen physicians and 20 allied health professionals. Methods: Six focus group discussions were conducted at three different sites during 2014 and 2015. Transcripts were analyzed using a qualitative-descriptive approach involving analytic immersion in the data, reflection, and achieving consensus around themes discerned from transcribed discussions. Results: Findings resolved into five main themes: 1) challenges of managing chronic pain in primary care; 2) ECHO participation and improvement in patient-provider interaction and participant knowledge; 3) the diffusion of knowledge gained through ECHO to participants' colleagues and patients; 4) ECHO participation generating a sense of community; and 5) disadvantages associated with participating in ECHO. Conclusions: Managing patients with chronic pain in primary care can be difficult, particularly in remote or underserved practices. Project ECHO offers guidance to primary care practitioners for their most challenging patients, promotes knowledge acquisition and diffusion, and stimulates the development of a "community of practice."
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".