Evidence map›Paper›PMID 42177278›Full record

ArticleScientific reports2026

Assessing nurses' knowledge and confidence in adult sepsis: evidence from two hospitals in eastern China.

Wang Pei, Ju Xueting, Liu Jiali, Hou Qingyuan, Jiang Guomei

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In one paragraph

Article in Scientific reports, 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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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Wang PeiDepartment of Nursing, Research Interests: Critical and Emergency Care, Jiangsu Provincial Hospital of Traditional Chinese Medicine, Nanjing City, 210000, Jiangsu Province, China.
Ju XuetingDepartment of Nursing, Jiangsu Provincial Hospital of Traditional Chinese Medicine, Jiangsu Nanjing City, 210000, Jiangsu Province, China.
Liu JialiDepartment of Nursing, Jiangsu Provincial Hospital of Traditional Chinese Medicine, Jiangsu Nanjing City, 210000, Jiangsu Province, China.
Hou QingyuanDepartment of Nursing, Jiangsu Provincial Hospital of Traditional Chinese Medicine, Jiangsu Nanjing City, 210000, Jiangsu Province, China.
Jiang GuomeiDepartment of Respiratory Infections and Critical Care Medicine, Nanjing Brain Hospital, Nanjing, 210000, Jiangsu, China. 54596801@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sepsis continues to pose a serious worldwide health issue, with considerable rates of illness and death, making early identification and rapid management essential for improving patient outcomes. Nurses, being the first point of patient care, have a pivotal responsibility in recognizing sepsis early and initiating timely interventions. Nevertheless, even with evidence-based frameworks like Sepsis-3 available, nurses still show knowledge deficiencies, reduced confidence, and inconsistent management practices, which can lead to delays in diagnosis and compromised care. This research set out to evaluate registered nurses' knowledge and self-rated confidence in identifying and handling adult sepsis, while also examining factors that may influence these skills. An online cross-sectional survey was carried out with 355 registered nurses from Nanjing Brain Hospital and Jiangsu Provincial Hospital of Traditional Chinese Medicine in eastern China. The survey included demographic data, a 15-item multiple-choice knowledge assessment aligned with Sepsis-3 guidelines, a 5-point Likert scale for confidence (range 5-25), and an open-ended item on organizational support needs. The data analysis employed descriptive statistics, independent t-tests, ANOVA, and multiple linear regression. The mean sepsis knowledge score was 10.04 ± 3.17 (out of 15), with notable gaps in recognizing early clinical signs and managing septic shock. Nurses practicing in high-acuity areas such as the ED, ICU, and the Respiratory Infection and Critical Care Unit (RICU) demonstrated significantly higher knowledge and confidence than ward-based nurses (p < .05). Higher education, senior job grade, and clinical exposure were independent predictors of better knowledge (R² = 0.156). The mean confidence score was 17.22 ± 3.62, showing a weak positive correlation with knowledge (r = .213, p = .007). Thematic analysis highlighted strong demand for structured sepsis training and standardized workflows. Significant gaps in sepsis knowledge and confidence persist among nurses, particularly in general wards. Integrating structured education, simulation-based training, and standardized protocols may enhance early recognition and improve patient outcomes.

Indexed as

Clinical CompetenceHealth Knowledge, Attitudes, PracticeNursesSepsisAdultChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedSurveys and QuestionnairesClinical confidenceEarly recognitionSepsis managementSepsis, Nurses’ knowledge

Identifiers

PMID42177278
PMCPMC13444126

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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.