Development and pilot of an interprofessional pediatric resuscitation program for non-acute care inpatient providers
Bibliographic record
Abstract
Multiprofessional ward healthcare providers are generally unprepared to assemble and engage in the initial resuscitation of pediatric inpatients. This is important as the performance of these first-responders, in the several minutes prior to the arrival of acute care support, may have significant effects on overall patient outcome. Accordingly, we aimed to develop and pilot a training program intended for non-acute care inpatient providers, relevant to their working context. Using the latest theory and evidence in medical education, we created an interprofessional, entirely in-situ, simulation-based small-group activity. The activity was then piloted for four months with the goals of assessing perceived usefulness, as well as implementation factors such as participant accessibility and overall resource requirements. A total of 37 interprofessional (physician and nursing) staff were trained in 16 small group sessions over four months. Post-participation questionnaires revealed that the activity was perceived to be highly useful for their practice; especially the rapid cycle deliberate practice instructional method, and the increased focus on crisis resource management. Resource requirements were comparable to, and perhaps less than, existing acute care training programs. This project describes the preliminary steps taken in creating a curriculum intended to improve interprofessional resuscitation performance across an institution.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".