Evidence map›Paper›PMID 42310682›Full record

ArticleBMC nursing2026

Effects of a closed-loop "Subspecialty-mind Mapping-Rolling Quiz" model based on cognitive load theory on the clinical decision-making ability of ICU nurses: a mixed-methods study.

Yao Ning Zhuang, Zhen Mei Xu, Dan Dan Lin, Qing Qing Zhan, Yi Yuan Chen, Bin Qin Lin, Shu Fen Ke

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Article in BMC nursing, 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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5 · Who and what money

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7 authors.

Yao Ning ZhuangDepartment of Respiratory Intensive Care Unit, Affiliated Hospital of Putian University, Putian, China.
Zhen Mei XuDepartment of Respiratory Intensive Care Unit, Affiliated Hospital of Putian University, Putian, China.
Dan Dan LinDepartment of Respiratory Intensive Care Unit, Affiliated Hospital of Putian University, Putian, China.
Qing Qing ZhanDepartment of Respiratory Intensive Care Unit, Affiliated Hospital of Putian University, Putian, China.
Yi Yuan ChenDepartment of Respiratory Intensive Care Unit, Affiliated Hospital of Putian University, Putian, China.
Bin Qin LinDepartment of Respiratory Intensive Care Unit, Affiliated Hospital of Putian University, Putian, China.
Shu Fen KeNursing Department, Affiliated Hospital of Putian University, Putian, China. 277573360@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn intensive care unit (ICU), traditional training models fail to meet the integrated cognitive demands. Additionally, subspecialty training and mind mapping have rarely been applied with a unifying theoretical framework for knowledge retention.

aimsBased on the Cognitive Load Theory and experiential learning cycle, this study aimed to construct and validate a closed-loop training model (Subspecialty-Mind Mapping-Rolling Quiz) to enhance ICU nurses' clinical decision-making, knowledge retention, self-efficacy and critical thinking.

designAn explanatory sequential mixed-methods design.

methodsThe quantitative phase was a cluster randomized controlled trial conducted in a tertiary teaching hospital in China. A total of 196 nurses from 4 ICU units were randomly assigned by unit to either an intervention group (2 units, n = 102) receiving the closed-loop training or a control group (2 units, n = 94) receiving traditional training from January to September 2025. Participants' Lasater Clinical Judgment Rubric (LCJR), theoretical and practical examination, General Self-Efficacy Scale (GSES), Critical Thinking Disposition Inventory-Chinese Version (CTDI-CV) scores, and knowledge retention rates were compared at pre-intervention, post-intervention (6 months), and at a 3-month follow-up. All 196 nurses completed the study. In the qualitative phase, 16 participants were sampled for semi-structured interviews.

resultsQuantitative results showed that the intervention group had significantly higher LCJR scores (t = 12.917, p < 0.01), post-intervention and delayed post-test theoretical and practical, GSES, and CTDI-CV scores (all p < 0.05). Qualitative analysis identified four themes: reconstruction of cognitive schema, shift in learning identity, sustained activation of metacognition and incentives coexisting with challenges in implementation.These themes provided insights into the mechanisms behind the quantitative improvements.

conclusionThe Subspecialty-Mind Mapping-Rolling Quiz enhanced ICU nurses' clinical decision-making, knowledge retention, self-efficacy, and critical thinking ability. By fostering knowledge internalization and contextualized retrieval, this model serves as an effective and theoretically grounded training solution. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Clinical decision-makingCluster randomized controlled trialCognitive load theoryIntensive care nursingMixed-methods researchNurse training

Identifiers

PMID42310682
PMCPMC13625216

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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.