“A Connected Community”: Evaluating the Use of Project ECHO to Support the Implementation of an Evidence-Based Early Psychosis Care Model
Bibliographic record
Abstract
Background and Hypothesis: (EPI-SET) is a study evaluating the implementation and impact of NAVIGATE, a manualized model of care for first episode psychosis in geographically diverse EPI programs in Ontario. Project Extension for Community Health Outcomes (ECHO), a virtual training and capacity-building model, was used to support the NAVIGATE implementation for participating programs. We evaluated ECHO EPI-SET in supporting this implementation. Methods: Using Moore's Evaluation Framework for Continuing Education, attendance and biweekly surveys were used to evaluate clinician engagement and satisfaction with ECHO. A self-reported survey was used to assess self-efficacy across core competencies, determine whether ECHO changed their practice, and describe the nature of any changes. Semi-structured interviews focusing on participants' experience with ECHO EPI-SET were analyzed using thematic analysis. Results: A total of 92 participants from 6 EPI sites participated across 3 cycles of ECHO EPI-SET. Mean satisfaction ratings were high (>4/5 on a Likert scale). Participants who worked longer at their sites reported higher rates of self-efficacy. The interviews identified 5 major themes: creating a community of practice; supporting NAVIGATE; change in practice/application; implementation support; and strengths and areas for improvement. Conclusions: This is the first published evaluation of using ECHO in supporting the implementation of a model of EPI care. Participation in ECHO was associated with high levels of clinician satisfaction, engagement, and self-efficacy. Qualitative data suggest that ECHO supported the development of a community of practice, learning, and practice change and may be a helpful tool to support future implementations of NAVIGATE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".