Adapting the Consolidated Framework for Implementation Research to Create Organizational Readiness and Implementation Tools for Project ECHO
Bibliographic record
Abstract
The Project Extension for Community Healthcare Outcomes (ECHO) model expands primary care provider (PCP) capacity to manage complex diseases by sharing knowledge, disseminating best practices, and building a community of practice. The model has expanded rapidly, with over 140 ECHO projects currently established globally. We have used validated implementation frameworks, such as Damschroder's (2009) Consolidated Framework for Implementation Research (CFIR) and Proctor's (2011) taxonomy of implementation outcomes, combined with implementation experience to (1) create a set of questions to assess organizational readiness and suitability of the ECHO model and (2) provide those who have determined ECHO is the correct model with a checklist to support successful implementation. A set of considerations was created, which adapted and consolidated CFIR constructs to create ECHO-specific organizational readiness questions, as well as a process guide for implementation. Each consideration was mapped onto Proctor's (2011) implementation outcomes, and questions relating to the constructs were developed and reviewed for clarity. The Preimplementation list included 20 questions; most questions fall within Proctor's (2001) implementation outcome domains of "Appropriateness" and "Acceptability." The Process Checklist is a 26-item checklist to help launch an ECHO project; items map onto the constructs of Planning, Engaging, Executing, Reflecting, and Evaluating. Given that fidelity to the ECHO model is associated with robust outcomes, effective implementation is critical. These tools will enable programs to work through key considerations to implement a successful Project ECHO. Next steps will include validation with a diverse sample of ECHO projects.
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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 | 0.462 | 0.439 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.008 | 0.019 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".