Building Mental Health Capacity: Exploring the Role of Adaptive Expertise in the ECHO Virtual Learning Model
Bibliographic record
Abstract
INTRODUCTION: With the proliferation of virtual learning programs during the COVID-19 pandemic, there is increased need to understand learner experiences and impact on developing expertise. Project Extension for Community Healthcare Outcomes (Project ECHO®) is an established hub-and-spoke tele-education model aimed at building capacity and expertise in primary care providers. Our qualitative study explored how learning experiences within an ECHO mental health care program supported provider learning and ability to solve complex clinical problems. METHODS: We sampled ECHO sessions across a 34-week cycle and analyzed audio transcribed data. Two individuals coded participant interactions during 2-hour recorded sessions using an iterative, constant comparative methodology. RESULTS: The authors identified four key mechanisms of learning in ECHO: (1) fostering participants' productive struggle with cases, (2) development of an integrated understanding, (3) collaborative reformulation of cases, and (4) generation of conceptual solutions based on a new understanding. Throughout the ECHO sessions, learning was observed to be multidirectional from both the hub-to-spoke and between spoke sites. DISCUSSION: Despite the widespread implementation of Project ECHO and other virtual learning models, a paucity of research has focused on mechanisms of virtual learning within these models. Our study demonstrated a bidirectional exchange of knowledge between hub specialist teams and primary care provider spokes that aligned with the development of adaptive expertise through specific learning experiences in Project ECHO. Moreover, the ECHO structure may further support the development of adaptive expertise to better prepare participants to address patients' complex mental health needs.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".