Enhancing Pediatric Long COVID Care Through Telementoring: Insights From an ECHO Program
Bibliographic record
Abstract
Long COVID affects a significant number of children, yet clinician knowledge gaps and limited access to specialized care hinder effective management. With fewer than 20 pediatric long COVID clinics in the United States, many families must travel long distances for care. To address these challenges, a pediatric long COVID ECHO (Extension for Community Healthcare Outcomes) program was developed to educate health care professionals on evidence-based care. The program engaged 94 participants from the United States and Canada via weekly tele-education sessions, recruited through word of mouth and professional listservs. Pre-surveys (41% response rate) and post-surveys (29% response rate) were sent to attendees. Participants reported statistically significant improvements in knowledge, confidence, competence, and self-efficacy ( P < 0.001). This program represents a valuable initiative to facilitate timely interventions and empower primary care and community providers in diagnosing, treating, and managing long COVID in pediatric populations.
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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 | 0.004 | 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".