Medical Simulation in Toxicology: Enhancing Clinical Competence, Decision- Making, and Patient Care in Resource-Limited Settings

Medical simulation has emerged as a transformative approach in toxicology education. By providing immersive, hands-on experience, simulation enhances learner engagement, critical thinking, and long-term knowledge retention while offering a safe environment to practice clinical decision-making without jeopardizing patient safety. Despite these advantages, the integration of simulation-based training remains limited in low- and middle-income countries (LMICs) due to resource constraints, lack of standardized curricula, and limited access to high-fidelity simulators. Additionally, little research has explored its optimal application in these settings. This review explores available evidence regarding the effectiveness of medical simulation in the diagnosis and treatment of toxicological emergencies in LMICs. Our findings suggest that while traditional lecture-based instruction may provide immediate knowledge gains, several studies suggest simulation-based training may enhance long-term retention and practical skills, particularly in resource-limited environments. This review highlights the need for further research to develop cost-effective, scalable simulation models tailored to LMICs and evaluate their impact on clinical outcomes in toxicology education.

Authors

Publication Details

Journal
Journal of Nepal Medical Association
Published
2026-04-30
DOI
https://doi.org/10.31729/jnma.v64i297.9442
Primary Topic
Simulation-Based Education in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Medical Simulation in Toxicology: Enhancing Clinical Competence, Decision- Making, and Patient Care in Resource-Limited Settings

Rakesh Ghimire, Sanjeev Kharel, Allan J. Hamilton, Farshad M. Shirazi et al.
Journal of Nepal Medical Association
Simulation-Based Education in Healthcare
article

Medical Simulation in Toxicology: Enhancing Clinical Competence, Decision- Making, and Patient Care in Resource-Limited Settings

Rakesh Ghimire, Sanjeev Kharel, Allan J. Hamilton, Farshad M. Shirazi, Anjana Thapa, Shristi Kharel Ghimire, Rabin Basnet
article en

Abstract

Medical simulation has emerged as a transformative approach in toxicology education. By providing immersive, hands-on experience, simulation enhances learner engagement, critical thinking, and long-term knowledge retention while offering a safe environment to practice clinical decision-making without jeopardizing patient safety. Despite these advantages, the integration of simulation-based training remains limited in low- and middle-income countries (LMICs) due to resource constraints, lack of standardized curricula, and limited access to high-fidelity simulators. Additionally, little research has explored its optimal application in these settings. This review explores available evidence regarding the effectiveness of medical simulation in the diagnosis and treatment of toxicological emergencies in LMICs. Our findings suggest that while traditional lecture-based instruction may provide immediate knowledge gains, several studies suggest simulation-based training may enhance long-term retention and practical skills, particularly in resource-limited environments. This review highlights the need for further research to develop cost-effective, scalable simulation models tailored to LMICs and evaluate their impact on clinical outcomes in toxicology education.

Journal of Nepal Medical AssociationVol. 64(297)
Openalex Percentile: Top 9%
Simulation-Based Education in Healthcare
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.