Training of Pediatric Critical Care Providers in Developing Countries in Evidence Based Medicine Utilizing Remote Simulation Sessions
Bibliographic record
Abstract
Background. Remote simulation training provides a unique opportunity to captivate providers despite language, distance, and cultural barriers. Previously we developed a novel electronic decision support and rounding tool, the Checklist for Early Recognition and Treatment of Acute Illness in Pediatrics (CERTAINp). This study was conducted to determine the feasibility and impact of remote simulation training of international PICU providers using CERTAINp. Methods. We conducted train-the-trainer sessions in 7 hospitals based in 5 countries (China, Congo, Croatia, India, and Turkey) between 11/2015 and 11/2016. Providers first took part in a base line simulation session to assess their clinical performance. They had structured hands-on training using CERTAINp, which was done remotely using video conference with recording capabilities. Performance in PICU “admission” and “rounding” scenarios was assessed by their adherence to standard of care guidelines using CERTAINp. After this training, the providers were re-evaluated for performance using a validated instrument by 2 independent trained reviewers. Results. A total of 7 hospitals completed both baseline and post simulation sessions. We observed improved critical task (total 14) completion in the admission scenarios where pre training task completion was 8.2 ± 2.6, while after remote training was 11.2 ± 1.8, P = .01. In rounding scenarios, compliance to standard of care guidelines improved overall from 45% to 95% ( P < .01). Conclusion. We observed an improvement in compliance for measures determined as best practice guidelines in simulation rounding and overall improvement in critical tasks for simulated admission cases after remote training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".