Exploring the Implementation of Multiple Telementoring ECHO Programs From an Institutional and Organizational Perspective: Qualitative Study
Bibliographic record
Abstract
Background: Project Extension for Community Healthcare Outcomes (ECHO) is an innovative model to increase capacity to treat patients in their community. Despite a growing body of evidence supporting its effectiveness, little is known about the implementation processes of multiple ECHO programs within an institution from the perspective of executives and institutional leaders. Objective: The study objective was to explore from an institutional and organizational standpoint the systemic characteristics that influence the implementation of Project ECHO programs, their growth within an ecosystem, and their sustainability. Methods: Focus groups and individual interviews were carried out with executives and leaders from an institution that implemented 3 Project ECHO programs, and verbatim were analyzed based on organizational readiness and implementation tools for Project ECHO. Results: This study highlighted the rarely reported perspectives of executives and institutional partners, shedding light on the organizational components that are essential to the deployment and sustainability of Project ECHO. Results reflect the intricate balance between institutional resources and its broader mission within a provincial, public health care system. In terms of acceptability, the fit between the projects and the institution's values of innovation, contribution to the broader community, and improving patient trajectory was central from the organizational leaders' standpoint. The structure of the projects and their rapid growth within the institution confirmed the adequacy with the institution. The projects benefited from temporary funds initially, and the lack of performance indicators that were easily measurable and the lack of recognition for invested time from clinicians were barriers to moving toward sustainability. Organizational characteristics, including a decentralized management structure and ministerial support for innovative educational practices, increased the perceived feasibility of implementing and maintaining these programs. Conclusions: This qualitative study of institution leaders and directors highlighted the challenges and facilitators to the deployment of an innovative continuous education model aimed at building capacity in the community for the management of various health conditions. Despite limitations, such as temporary initial funding, challenges in collecting performance indicators, most valued, and rigidity of the projects' structure, results also show many characteristics (innovative model, alignment with the institution's mission, and simplicity of its deployment) that helped move these projects toward sustainability within the institution. Results offer learning experiences that will be relevant to other settings evolving within a similar public health care system, wanting to implement this model.
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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 | 0.018 | 0.019 |
| 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 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".