Exploring the impact of the ECHO model™ in Ontario on primary healthcare providers sharing of chronic pain knowledge: A qualitative study
Bibliographic record
Abstract
ECHO Ontario Chronic Pain/Opioid Stewardship (ECHO Ontario Pain) is a telehealth platform, which supports healthcare providers (HCPs, spokes) to manage patients with chronic pain in their communities, using specialists (hub). ECHO Ontario Pain is using this model to address challenges, such as dealing with a lack of knowledge about chronic pain and inappropriate opioid prescribing practices. Thirteen qualitative semi-structured interviews were conducted with HCPs from the program. Four themes developed: (1) experiences with chronic pain management before joining ECHO, (2) learning and sharing in the program, (3) the use of technology, and (4) recommendations for improvements. ECHO Ontario Pain was a novel way to provide education by demonstrating the effectiveness of participating in an online learning model. The study highlights the value of different learning approaches and how they affect HCPs interactions with their patients, their practices, and their wider community. Overall, these findings complement and add to existing ECHO research.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".