Implementation of Project ECHO in a university health network: contrasting and comparing experiences across health conditions through a qualitative approach in a Canadian tertiary care centre
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to compare and contrast the experiences of interdisciplinary attendees (spokes) and experts (hub members) from three Extension for Community Healthcare Outcomes (ECHO) programmes: hepatitis C, chronic pain and concurrent mental health and substance use disorders. DESIGN: Prospective qualitative study. SETTING: Single-centre in tertiary care. PARTICIPANTS: The team conducted 30 one-on-one interviews with spokes and 4 focus groups with hub members from three ECHO programmes. ANALYSES: Three analysts were involved to perform a reflexive thematic analysis. RESULTS: Our results showed the benefits and limitations of the three ECHOs, varying according to specificities of targeted chronic conditions. Three overarching themes were identified from the data analysis: (1) perceived impacts of an interprofessional educational setting; (2) nature of disease and interprofessional interactions as determinants of clinical practice changes in diagnoses and treatments and (3) impacts on patient engagement and care pathways. CONCLUSIONS: The extent to which a chronic disease relies on a biopsychosocial approach, the degree of interdisciplinary care required and the simplicity/complexity of treatment algorithms influence perceived benefits and barriers to participating in ECHO programmes. These points raised by our study are important in the understanding of the successes and limitations of implementing an ECHO programme. They are essential as they provide key information for tailoring Project ECHO to the chronic disease it addresses.
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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 | 0.025 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".