心理干预
批判性评价
定性研究
主题分析
包裹体(矿物)
医学
感知
患者安全
定性性质
护理部
梅德林
心理学
医疗保健
应用心理学
替代医学
社会心理学
计算机科学
法学
机器学习
神经科学
经济
社会学
病理
经济增长
社会科学
政治学
作者
Ginger Schroers,Jennifer Gunberg Ross,Helene Moriarty
标识
DOI:10.1016/j.jcjq.2020.09.010
摘要
Medication administration errors (MAEs) are a critical patient safety issue. Nurses are often responsible for administering medication to patients, thus their perceptions of causes of errors can provide valuable guidance for the development of interventions aimed to mitigate errors. Quantitative research can overlook less overt causes; therefore, a qualitative systematic review was conducted to present a synthesis of qualitative evidence of nurses’ perceived causes of MAEs. Publications from 2000 to February 2019 were searched using four electronic databases. Inclusion criteria were articles that (1) presented results from studies that used a qualitative or mixed methods design, (2) reported qualitative data on nurses’ perceived causes of MAEs in health care settings, and (3) were published in the English language. Sixteen individual articles satisfied the inclusion criteria. Methodological quality of each article was assessed using the Critical Appraisal Skills Programme (CASP) tool. Thematic analysis of the data was performed. Perceived causes of errors were labeled as knowledge-based, personal, and contextual factors. The primary knowledge-based factor was lack of medication knowledge. Personal factors included fatigue and complacency. Contextual factors included heavy workloads and interruptions. Contextual factors were reported in all the studies reviewed and were often interconnected with personal and knowledge-based factors. Causes of MAEs are perceived by nurses to be multifactorial and interconnected and often stem from systems issues. Multifactorial interventions aimed at mitigating medication errors are required with an emphasis on systems changes. Findings in this review can be used to guide efforts aimed at identifying and modifying factors contributing to MAEs.
科研通智能强力驱动
Strongly Powered by AbleSci AI