The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Using Debriefing for Meaningful Learning to Foster Development of Clinical Reasoning in Simulation
Abstract
Debriefing is critical to learning from simulation experiences, yet the literature reports little research describing best practices within nursing. Debriefing for Meaningful Learning (DML) is a systematic process for debriefing in which teachers and students explicate different aspects of reflection and generate new meanings from simulation experiences. The purpose of this exploratory, quasi-experimental, pretest–posttest study was to test the relationship of DML on the development of clinical reasoning skills in prelicensure nursing students when compared with customary debriefing strategies and on students’ perception of quality of the debriefing experience. Analysis of data demonstrated a greater change in clinical reasoning skills and identification of higher-quality debriefing and a positive correlation between clinical reasoning and perception of quality. Findings demonstrate that DML is an effective debriefing method. It contributes to the body of knowledge supporting the use of debriefing in simulation learning and supports the development of best teaching practices.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Statistical power analyses using G∗Power 3.1: tests for correlation and regression analyses
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009) · Behavior Research Methods · doi:10.3758/brm.41.4.1149 · Cited by 37,615
The reflective practitioner: How professionals think in action
Stewart, C. J. (1984) · Patient Education and Counseling · doi:10.1016/0738-3991(84)90022-3 · Cited by 19,752
Thinking Like a Nurse: A Research-Based Model of Clinical Judgment in Nursing
Tanner, C. A. (2006) · Journal of Nursing Education · doi:10.3928/01484834-20060601-04 · Cited by 1,735
The Role of Debriefing in Simulation-Based Learning
Fanning, R. M., & Gaba, D. M. (2007) · Simulation in Healthcare The Journal of the Society for Simulation in Healthcare · doi:10.1097/sih.0b013e3180315539 · Cited by 1,714
Design Sensitivity: Statistical Power for Experimental Research.
Eliason, S. R., & Lipsey, M. W. (1991) · Contemporary Sociology · doi:10.2307/2072167 · Cited by 1,040
Rote Versus Meaningful Learning
Mayer, R. E. (2002) · Theory Into Practice · doi:10.1207/s15430421tip4104_4 · Cited by 877
Debriefing with Good Judgment: Combining Rigorous Feedback with Genuine Inquiry
Rudolph, J. W., Simon, R., Rivard, P., Dufresne, R. L., & Raemer, D. B. (2007) · Anesthesiology Clinics · doi:10.1016/j.anclin.2007.03.007 · Cited by 807
Simulation in Nursing Education: From Conceptualization to Evaluation
Jeffries, P. R. (2007) · Cited by 782
Debriefing as Formative Assessment: Closing Performance Gaps in Medical Education
Rudolph, J. W., Simon, R., Raemer, D. B., & Eppich, W. J. (2008) · Academic Emergency Medicine · doi:10.1111/j.1553-2712.2008.00248.x · Cited by 771
High-Fidelity Simulation and the Development of Clinical Judgment: Students' Experiences
Lasater, K. (2007) · Journal of Nursing Education · doi:10.3928/01484834-20070601-06 · Cited by 548
Cited by
Debriefing for technology-enhanced simulation: a systematic review and meta-analysis
Cheng, A., Eppich, W., Grant, V., Sherbino, J., Zendejas, B., & Cook, D. A. (2014) · Medical Education · doi:10.1111/medu.12432 · Cited by 481
Johnsen, H. M., Fossum, M., Vivekananda-Schmidt, P., Fruhling, A., & Slettebø, Å. (2016) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2016.06.014 · Cited by 141
Gu, Y., Zou, Z., & Chen, X. (2017) · Clinical Simulation in Nursing · doi:10.1016/j.ecns.2017.01.005 · Cited by 104
Clinical reasoning and clinical judgment in nursing research: A bibliometric analysis
Doyon, O., & Raymond, L. (2025) · International Journal of Nursing Knowledge · doi:10.1111/2047-3095.12484 · Cited by 13
Metadata
- Authors
- Dreifuerst, K. T.
- Venue
- Journal of Nursing Education
- Publisher
- SLACK, Inc.
- Published
- 2012-06
- Volume
- 51
- Issue
- 6
- Pages
- 326-333
- ISSN
- 0148-4834 (ISSN-L)
- 1938-2421
- OpenAlex
- W1985749939
- Cited by
- 366
- References
- 29
- FWCI
- 11.32
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Using Debriefing for Meaningful Learning to Foster Development of Clinical Reasoning in Simulation. https://app.cochat.ai/papers/10.3928/01484834-20120409-02
- The paper
- Dreifuerst, K. T. (2012). Using Debriefing for Meaningful Learning to Foster Development of Clinical Reasoning in Simulation. Journal of Nursing Education. https://doi.org/10.3928/01484834-20120409-02
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
