Evolving Project ECHO: delivery of pediatric pain core competency learning for interprofessional healthcare providers
Bibliographic record
Abstract
Introduction Healthcare providers (HCPs) practicing in community settings are critical to improving access to pain care, yet there are significant gaps in training opportunities designed for interprofessional learners. Project Extension for Community Healthcare Outcomes (Project ECHO®) is an established model for delivering online HCP education through virtual clinics and cultivating a community of practice. However, to our knowledge, the integration of pain core competency education into the ECHO® model has not been previously attempted. This innovation could enhance the ECHO® model while also addressing the growing calls for more accessible interprofessional pain curricula. This paper describes efforts to implement and evaluate core competency curricula within the context of Pediatric Project ECHO for Pain, one of the first pediatric-pain focused ECHO programs in the world. Methods Needs assessments informed curricula development. The first delivered core competency model consisted of synchronous webinar-style sessions while the second model included a mixture of asynchronous (eLearning course) and synchronous (virtual clinical debrief) elements. A convenience sample of HCPs was recruited from ECHO program registrants. Participants completed baseline and follow-up surveys to assess core competency acceptability as well as impact on knowledge and self-efficacy related to managing pediatric pain. Usability of the eLearning platform (model 2 only) was also evaluated. Surveys used 5-point Likert scales to capture outcomes. A priori targets included mean scores ≥4/5 for acceptability and ≥80% of learners reporting knowledge and self-efficacy improvements. The study received local research ethics approval. Results The core competency was found to be highly acceptable to interprofessional learners (n = 31) across delivery models, surpassing a priori targets. Specifically, it was characterized as a worthwhile and satisfactory experience that was helpful in supporting learning. The core competency was also associated with improvements in knowledge and self-efficacy by 97% and 90% of learners, respectively. The eLearning platform was reported to have high usability with clinically realistic cases (100% of respondents) that were helpful to inform care delivery (94% of respondents). Conclusion The integration of core competency learning within the Project ECHO® model was a successful approach to deliver pediatric pain education to interprofessional HCPs.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".