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拓展我们对职业倦怠及其相关因素的理解:基层医疗诊所的医护人员

Extending Our Understanding of Burnout and Its Associated Factors: Providers and Staff in Primary Care Clinics.

作者信息

Spinelli William M, Fernstrom Karl M, Galos Dylan L, Britt Heather R

机构信息

Division of Applied Research, Allina Health, Minneapolis, MN, USA

Division of Applied Research, Allina Health, Minneapolis, MN, USA.

出版信息

Eval Health Prof. 2016 Sep;39(3):282-98. doi: 10.1177/0163278716637900. Epub 2016 Mar 21.

Abstract

Burnout has been identified as an occupational hazard in the helping professions for many years and is often overlooked, as health-care systems strive to improve cost and quality. The Maslach Burnout Inventory (MBI) and the Areas of Worklife Survey (AWS) are tools for assessing burnout prevalence and its associated factors. We describe how we used them in outpatient clinics to assess burnout for multiple job types. Traditional statistical techniques and seemingly unrelated regression were used to describe the sample and evaluate the association between work life domains and burnout. Of 838 eligible participants, 467 (55.7%) were included for analysis. Burnout prevalence varied across three job categories: providers (37.5%), clinical assistants (24.6%), and other staff (28.0%). It was not related to age, gender, or years of tenure but was lower in part-time workers (24.6%) than in full-time workers (33.9%). Analysis of the AWS subscales identified organizational correlates of burnout. Accurately identifying and defining the operative system factors associated with burnout will make it possible to create successful interventions. Using the MBI and the AWS together can highlight the relationship between system work experiences and burnout.

摘要

多年来,职业倦怠一直被视为助人行业的一种职业危害,而且常常被忽视,因为医疗保健系统致力于提高成本效益和质量。马氏职业倦怠量表(MBI)和工作生活领域调查(AWS)是评估职业倦怠患病率及其相关因素的工具。我们描述了如何在门诊诊所使用这些工具来评估多种工作类型的职业倦怠情况。我们运用传统统计技术和看似不相关回归来描述样本,并评估工作生活领域与职业倦怠之间的关联。在838名符合条件的参与者中,467名(55.7%)被纳入分析。职业倦怠患病率在三类工作岗位中有所不同:提供者(37.5%)、临床助理(24.6%)和其他工作人员(28.0%)。它与年龄、性别或任职年限无关,但兼职人员(24.6%)的职业倦怠患病率低于全职人员(33.9%)。对AWS分量表的分析确定了职业倦怠的组织相关因素。准确识别和定义与职业倦怠相关的操作系统因素将有助于制定成功的干预措施。同时使用MBI和AWS可以突出系统工作体验与职业倦怠之间的关系。

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