Pediatric Project ECHO: Implementation of a Virtual Medical Education Program to Support Community Management of Children With Medical Complexity
Bibliographic record
Abstract
OBJECTIVES: Health care providers (HCPs) require ongoing support to meet the evolving care needs of children with medical complexity (CMC). Project Extension for Community Healthcare Outcomes (ECHO) is a model for delivering technology-enabled medical education and cultivating a community of practice. In this study, we focused on developing, implementing, and evaluating the first ECHO program dedicated to the care of CMC. Specific objectives were to evaluate the program feasibility (participation and acceptability) and impact on perceived HCP knowledge, self-efficacy, and clinical practice after 6 months. METHODS: A needs assessment was conducted to inform an interprofessional CMC curriculum. This curriculum was delivered through monthly virtual TeleECHO clinics (didactic and case-based learning) from January 2018 to 2020. The program was available at no cost to HCPs throughout Ontario. Surveys were distributed at baseline and 6 months to assess program acceptability, knowledge, self-efficacy, and practice impact by using 7-point Likert scales. Descriptive and inferential data analyses were conducted. RESULTS: values ranged from <.001 to .006). These knowledge and self-efficacy scores related to "complex care support," "feeding support," and "respiratory support." The majority of participants reported positive or very positive practice impacts, including enhanced ability to provide quality care to CMC. CONCLUSIONS: Project ECHO is a feasible and acceptable model for virtual education of interprofessional HCPs in managing CMC. This program has the potential to increase system capacity to provide quality care to CMC close to home.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".