Evidence map›Paper›PMID 41963925›Full record

ArticleBMC medical education2026

Effectiveness of scenario-based high-fidelity simulation training on management of medical emergencies in dental settings: a theory-based intervention with a long-term follow-up.

Yasmine N Alawaji, Nesrin A Alharthy, Abdulrhman Alghamdi, Abdullah Alshibani, Meshary Binhotan, Hussam Alqahtani, Farraj Albalawi, Aram Alshehri, Ali Aboalela, Sahr H Altuwaijri and 2 more

Abstract read
In one paragraph

Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Yasmine N AlawajiPreventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia. Awajiy@ksau-hs.edu.sa.ORCID http://orcid.org/0000-0002-4038-549X
Nesrin A AlharthyKing Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
Abdulrhman AlghamdiKing Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
Abdullah AlshibaniKing Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
Meshary BinhotanKing Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
Hussam AlqahtaniPreventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.
Farraj AlbalawiPreventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.
Aram AlshehriKing Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
Ali AboalelaKing Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
Sahr H AltuwaijriKing Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
Sara A KalagiKing Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
Abdulmohsen AlfadleyKing Abdullah International Medical Research Center, Riyadh, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesSkills pertaining to management of medical emergencies learned though basic life support courses were reported to decline within a few months. Despite the effectiveness of scenario-based simulation training in managing medical emergencies in dental settings, the long-term stability of the findings remains unclear. The aim of this study is to evaluate the effectiveness of a theory- and scenario-based high-fidelity simulation training in managing common medical emergencies in dental settings.

methodsA single-day simulation training based on the social cognitive theory was conducted to train senior dental students at King Saud bin Abdulaziz University for Health Sciences in the management of common medical emergencies in dental settings. A refresher review and demonstration of cardiopulmonary resuscitation were provided to enhance the students’ behavioral capability (Knowing what and how to perform a certain behavior). In addition, four known approaches for enhancing the self-efficacy (Perceived confidence) were incorporated in designing the medical emergency scenarios to allow students’ learning through: a self-modeling (Learning from own experience), a vicarious modeling (Learning from peers and instructors’ experience), debriefing and feedback, and provoking engagement through detailed and realistic scenarios utilizing high-fidelity simulation to mimic real-life experience. A 13-item self-efficacy and 19-item behavioral capability validated scales were measured at baseline, immediately post-training, and at long-term (9 to 11 months) follow-up; a paired t-test was used for mean comparisons. The effectiveness of training was evaluated by the students for its importance, usefulness, recommendation to others, and possibility of registering in a similar course.

resultsA total of 34 students participated in the training course and 20 of them participated in the long-term follow-up. Statistically significant mean (SD) increased behavioral capability by 4.7 (1.5) points and self-efficacy by 41.4 (8.3) were found immediately post-training. Negligible and non-statistically significant declines in behavioral capability and self-efficacy were found in 9 to 11 months post-training; indicating long-term stability. Students’ assessment of the training course regarding its importance, usefulness, and recommendation ranged from 97.0 to 99.0%.

conclusionsThe high-fidelity simulation training was effective in increasing the behavioral capability and self-efficacy of managing medical emergencies in dental settings and long-term stability was achieved. We recommend tailoring the training on managing medical emergencies to dental settings and utilizing scenarios that mimics real-life experiences to enhance the self-efficacy and behavioral capability in managing medical emergencies.

Indexed as

Cardiopulmonary ResuscitationClinical CompetenceEducation, DentalHigh Fidelity Simulation TrainingSimulation TrainingEmergenciesFollow-Up StudiesHumansSelf EfficacyStudents, DentalBehavioral capabilityBehavioral theoryMedical emergencySelf-efficacySimulation

Identifiers

PMID41963925
PMCPMC13217964

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LicenceCC BY-NC-ND
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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.