Simulation Platforms to Train and Assess Pediatric Acute Care Procedural Skills: A Scoping Review
Bibliographic record
Abstract
ABSTRACT Background Medical trainees have limited opportunities to practice certain pediatric emergency medicine (PEM) procedures in the clinical setting. Simulation‐based education provides an opportunity for trainees to improve their technical and non‐technical skills. This scoping review aimed to explore the ways in which simulation has been used to train and assess medical learners. Methods A search was conducted from 2011 to August 2022 using MEDLINE, OVID Embase, OVID Emcare, and Cochrane Trials, among others. The review included empirical studies that used simulation for training medical trainees in four essential pediatric procedures: intubation, lumbar puncture, intraosseous insertion, and chest tube insertion. Six reviewers independently screened titles, abstracts, and full texts. Data were recorded, stored, and summarized in a standardized spreadsheet. Results The search retrieved 4073 articles, of which 207 underwent full‐text screening. Data were extracted from 107 studies. Among these, 69 studies focused exclusively on intubation skills, 19 studies on lumbar puncture, 10 studies on intraosseous insertion, and two studies on chest tube insertion. The majority of studies (n = 61) involved residents, while 15 studies focused on medical students. Training platforms included mannequins (n = 84), ex‐vivo models (n = 6), task trainers (n = 4), and standardized patients (n = 1). Competency was largely assessed using an assessment tool (n = 45) as the sole assessment method. Conclusions Simulation‐based training and assessment can support medical learners in developing key technical and non‐technical skills related to PEM procedures. However, there is a need for diversification of simulation platforms used, expansion of procedures targeted in simulation‐based training and assessment programs, and creation of standardized and procedure‐specific assessments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".