Cognitive biases in nursing students' clinical decision-making: insights from high-fidelity simulation using think-aloud and observational methods
- 1chool of Midwifery and Community Health, Anglia Ruskin University, Cambridge, United Kingdom
Abstract
BackgroundCognitive biases are systematic errors in human reasoning that can compromise clinical decision-making (CDM) in healthcare. While extensively studied in medicine, nursing research on cognitive biases in CDM remains limited.AimThis study aimed to explore and identify cognitive biases and examine their relationship with nursing students' clinical decision-making during a high-fidelity simulation of a deteriorating patient scenario.DesignA mixed-methods design was employed, incorporating concurrent and retrospective think-aloud (TA) protocols and observational data.MethodsTwenty-three final-year Bachelor of Science nursing students participated in a high-fidelity, manikin-based simulation involving a clinically deteriorating patient scenario. A catalogue of 12 cognitive biases was developed from the literature and used to code statements from concurrent and retrospective think-aloud protocols and observational data using content analysis.ResultsA total of 166 bias occurrences were identified across all data sources (63 concurrent TA; 51 retrospective TA; 52 observation). Representativeness was the most frequently identified bias overall, particularly in retrospective think-aloud data. Order effect was the most common bias in concurrent think-aloud data, and representativeness and order effect were the most common in observational data. Premature closure and availability were also prominent across all sources of data. Biases occurred proportionally across both Type 1 (automated/pattern recognition) and Type 2 (hypothetico-deductive) decision-making, with notable differences between concurrent and retrospective think-aloud findings. The findings also suggest that simulation may support students' awareness of cognitive bias.ConclusionHigh-fidelity simulation, combined with think-aloud protocols and observation, provided a useful approach for identifying cognitive biases in nursing students' clinical decision-making. The findings have implications for simulation design, debriefing practice, and nursing education.
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BibTeX
@article{abdulmohdi2026cognitive,
title = {{Cognitive biases in nursing students' clinical decision-making: insights from high-fidelity simulation using think-aloud and observational methods}},
author = {Naim Abdulmohdi},
journal = {Frontiers in Medicine},
year = {2026},
month = nov,
doi = {10.3389/fmed.2026.1984162},
url = {https://doi.org/10.3389/fmed.2026.1984162},
note = {CC BY},
}
RIS
TY - JOUR
TI - Cognitive biases in nursing students' clinical decision-making: insights from high-fidelity simulation using think-aloud and observational methods
AU - Naim Abdulmohdi
PY - 2026
DA - 2026/11/03
JO - Frontiers in Medicine
DO - 10.3389/fmed.2026.1984162
UR - https://doi.org/10.3389/fmed.2026.1984162
AB - BackgroundCognitive biases are systematic errors in human reasoning that can compromise clinical decision-making (CDM) in healthcare. While extensively studied in medicine, nursing research on cognitive biases in CDM remains limited.AimThis study aimed to explore and identify cognitive biases and examine their relationship with nursing students' clinical decision-making during a high-fidelity simulation of a deteriorating patient scenario.DesignA mixed-methods design was employed, incorporating concurrent and retrospective think-aloud (TA) protocols and observational data.MethodsTwenty-three final-year Bachelor of Science nursing students participated in a high-fidelity, manikin-based simulation involving a clinically deteriorating patient scenario. A catalogue of 12 cognitive biases was developed from the literature and used to code statements from concurrent and retrospective think-aloud protocols and observational data using content analysis.ResultsA total of 166 bias occurrences were identified across all data sources (63 concurrent TA; 51 retrospective TA; 52 observation). Representativeness was the most frequently identified bias overall, particularly in retrospective think-aloud data. Order effect was the most common bias in concurrent think-aloud data, and representativeness and order effect were the most common in observational data. Premature closure and availability were also prominent across all sources of data. Biases occurred proportionally across both Type 1 (automated/pattern recognition) and Type 2 (hypothetico-deductive) decision-making, with notable differences between concurrent and retrospective think-aloud findings. The findings also suggest that simulation may support students' awareness of cognitive bias.ConclusionHigh-fidelity simulation, combined with think-aloud protocols and observation, provided a useful approach for identifying cognitive biases in nursing students' clinical decision-making. The findings have implications for simulation design, debriefing practice, and nursing education.
SN - 2296-858X
ER -