Cited by 8Simulation-Based Education in HealthcareInnovations in Medical EducationSurgical Simulation and Training

Undergraduate nursing students' perceptions of debriefing sessions in simulation-based learning: A systematic review

Open access
Glennie, Leanne·2025·Teaching and Learning in Nursing

Abstract

• Debriefing is a deep reflective learning process within simulation-based learning. • Consider learner preferences of reflection for it to be meaningful and effective. • Learners prefer 2-stepped approach such as self-reflection and group discussions. The aim of this systematic review is to determine how debriefing processes, perceived by undergraduate nursing students, affect confidence and learning outcomes from qualitative studies conducted between 2019 and 2023. This systematic review is aligned with the PRSIMA 2020 search strategy and includes data from only qualitative studies. Previous studies on perceived outcomes surrounding effective debriefing processes within simulation-based learning has predominantly focused on quantitative and mixed method studies. A total of 358 primary studies were obtained from the initial search: CINAHL (n = 64), ERIC (n = 3), MEDLINE (ProQuest) (n = 42), SCOPUS (n = 101), and WEB OF SCIENCE (n = 137); and hand searched articles (n = 11). A total of 5 studies were included in the review from which 3 underlying commonalities were identified: Self-awareness and self-confidence; Safe Learning Environment; and Confidence for Clinical Practice. The debriefing process is perceived as having a positive effect on students’; however, a 2-step approach to debriefing in which self-reflection prior to group discussion on their performance is preferred.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.teln.2025.03.018

Cites

24 of 24 references verified0 citation errors · 0 not checkable

Statistics

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.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. Eom, M., Kim, S., & Kim, O. (2021). Design and implementation of blended learning approach for simulation education among undergraduate nursing simulation. Turkish Journal of Computer and Mathematics Education, 12 (6), 733 -742. http://refhub.elsevier.com/S1557-3087(2500114-3/sbref0015)

Cited by

Sorted by citations

Authors
Glennie, L.
Affiliations
University of Strathclyde (GB)
Corresponding author
Glennie, Leanne
Venue
Teaching and Learning in Nursing
Publisher
Elsevier BV
Published
2025-07
Volume
20
Issue
3
Pages
e907-e914
ISSN
  • 1557-2013 (ISSN-L)
  • 1557-3087
OpenAlex
W4409676336
Cited by
8
References
24
Citations by year
2026: 6 · 2025: 2
FWCI
1.05
Type
journal article
Language
en
Open access
Locations
  • Teaching and learning in nursing (publishedVersion, cc-by)
  • Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) (submittedVersion)
Topics
  • Health Sciences › Medicine › Physiology › Simulation-Based Education in Healthcare
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
  • Health Sciences › Medicine › Surgery › Surgical Simulation and Training
Keywords
debriefing, simulation-based training, undergraduate nursing students, student perceptions, self-reflection, group discussion, self-confidence, self-awareness, safe learning environment, reflective learning

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