Evidence map›Paper›PMID 37560048›Full record

ArticleGMS journal for medical education2023

Successful implementation of a rater training program for medical students to evaluate simulated pediatric emergencies.

Nadine Mand, Tina Stibane, Helmut Sitter, Rolf Felix Maier, Andreas Leonhardt

Abstract read
In one paragraph

Article in GMS journal for medical education, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Generative AI (gAI) in medical education: Chat-GPT and co.GMS journal for medical education · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Nadine MandPhilipps University of Marburg, University Hospital Marburg, Department of Pediatrics, Marburg, Germany.
Tina StibanePhilipps University of Marburg, Dr. Reinfried Pohl Center for Medical Education, Marburg, Germany.
Helmut SitterPhilipps University of Marburg, Dr. Reinfried Pohl Center for Medical Education, Marburg, Germany.
Rolf Felix MaierPhilipps University of Marburg, University Hospital Marburg, Department of Pediatrics, Marburg, Germany.
Andreas LeonhardtPhilipps University of Marburg, University Hospital Marburg, Department of Pediatrics, Marburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Simulation-based training is increasingly used in pediatrics to teach technical skills, teamwork, and team communication, and to improve potential deficiencies in pediatric emergency care. Team performance must be observed, analyzed, and evaluated by trained raters. The structured training of medical students for the assessment of simulated pediatric emergencies has not yet been investigated. Methods: We developed a rater training program for medical students to assess guideline adherence, teamwork, and team communication in simulated pediatric emergencies. Interrater reliability was measured at each training stage using Kendall tau coefficients. Results: In 10 out of 15 pairs of raters interrater reliability was moderate to high (tau>0.4), whereas it was low in the remaining 5 pairs of raters. Discussion: The interrater reliability showed good agreement between medical students and expert raters at the end of the rater training program. Medical students can be successfully involved in the assessment of guideline adherence as well as teamwork and team communication in simulated pediatric emergencies.

Indexed as

EmergenciesStudents, MedicalChildClinical CompetenceHumansPatient Care TeamReproducibility of Resultsmedical studentspediatric life supportsimulation

Identifiers

PMID37560048
PMCPMC10407587

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.