Evidence map›Paper›PMID 40783750›Full record

ArticleBMC nursing2025

Investigating the prevalence of medication near-misses and reporting intention among nurses in tertiary hospitals from China.

Jinxia Fang, Zeyi Zhang, Qi Zhang, Wanshun Jia, Xue Chen, Yuanyuan Li

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Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jinxia Fang *Shandong Provincial Hospital Affiliated to Shandong First Medical University, #246 JingWu Road, Jinan, 250021, China.
Zeyi Zhang *Shandong Provincial Hospital Affiliated to Shandong First Medical University, #246 JingWu Road, Jinan, 250021, China.
Qi Zhang *Shandong Provincial Hospital Affiliated to Shandong First Medical University, #246 JingWu Road, Jinan, 250021, China.
Wanshun Jia *Shandong Provincial Hospital Affiliated to Shandong First Medical University, #246 JingWu Road, Jinan, 250021, China.
Xue Chen *Shandong Provincial Hospital Affiliated to Shandong First Medical University, #246 JingWu Road, Jinan, 250021, China.
Yuanyuan LiShandong Provincial Hospital Affiliated to Shandong First Medical University, #246 JingWu Road, Jinan, 250021, China. yuanyuanquan7@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGlobally, medication near-misses are a public health issue as they often have serious consequences for patients and healthcare systems. Currently, few studies have investigated nurses' intention to report medication near-misses. Therefore, this study aims to examine the incidence of medication near-misses and factors influencing reporting intention among nurses in tertiary hospitals in Shandong Province, to fill the research gap in this area.

designA descriptive cross-sectional design.

methodsBetween March 2021 and December 2021, 2625 nurses in seven tertiary hospitals from Shandong Province, China were enrolled in the study, using convenience sampling. Information about medication near-misses, reporting intention and barriers was collected using self-reported questionnaire, subscales of the Adverse Event Reporting Intention Questionnaire, and Medication Near-Miss Reporting Barriers Scale. This study explored the association between potential determinants from the perspective of nurses and managers using generalized linear model.

resultsAbout 25.3% of nurses experienced medication near-misses, with a mean reporting intention score of 5.44 (standardized deviation=1.13). The main approach for reporting medication near-misses was verbal language (80.6%). Higher professional title (χ2=13.770, P = 0.003), lower educational level (χ2=6.722, P = 0.035), fewer weekly work hours (χ2=3.947, P = 0.047), no previous medication near-misses experience (χ2=46.517, P < 0.001), and not having siblings (χ2=4.150, P = 0.042) nurses showed a higher level of reporting intention. The manager's negative response (χ2=7.059, P = 0.008) and complex reporting system (χ2=17.120, P < 0.001) were associated with lower levels of reporting intention, primarily influenced by departmental categories (χ2=22.306, P = 0.002). DISCUSSION AND

conclusionThe results of this study show that nurses' medication near-misses reporting rate is relatively low, but their intention to report is relatively strong. Meanwhile, nurses' intention to report medication near-misses is influenced by multiple factors. This suggests that nursing managers should develop management strategies for near-miss events and actively provide training to enhance clinical nurses' willingness to report such events.

Indexed as

Influencing factorsMedication near-missesNurseReporting barriersReporting intention

Identifiers

PMID40783750
PMCPMC12335104

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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.