A Multi-Wave Study of Factors Associated With Resident Engagement, Depression, Burnout, and Stay Intent.

HCA healthcare journal of medicine Pub Date : 2024-06-01 eCollection Date: 2024-01-01 DOI:10.36518/2689-0216.1837
Anne M Brafford, Brendon Ellis, Greg Guldner, Gabrielle Riazi, Xitao Liu, Jessica C Wells, Jason T Siegel
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Abstract

Background: Many studies have documented the epidemic of mental ill-being among resident physicians, but fewer have focused on mental well-being or on guiding intervention design to make progress toward positive change in residency programs to support resident thriving. Informed by the job demands-resources model (JD-R) and positive psychology, the current study examines 4 potential predictors of residents' ill-being (burnout, depression) and well-being (engagement, stay intent) that are malleable and thus capable of change through intervention: psychological capital (PsyCap), supervising physicians' autonomy-supportive leadership style (ASL), social support, and meaningful work.

Methods: Three waves of data were collected between November 2017 and September 2018 at a large hospital system in the United States. Due to participant response rates, we were unable to conduct a planned longitudinal analysis. Therefore, for each wave, Bayesian regression analyses were used to examine cross-sectional relationships between the 4 predictors and each outcome.

Results: Although findings varied across the study's 3 waves, the outcomes were largely as expected. With only 1 exception (depressive symptoms in Wave 2), meaningful work significantly predicted all outcome variables in the expected direction across all 3 waves. PsyCap significantly predicted burnout, depressive symptoms, and engagement in the expected direction across all 3 waves. ASL significantly predicted engagement in the expected direction across all 3 waves, as well as depressive symptoms and stay intent in 2 waves, and burnout in 1 wave. Social support significantly negatively predicted depressive symptoms in all 3 waves and burnout in 1 wave.

Conclusion: Applying the JD-R framework and a positive psychology lens can open new pathways for developing programming to support resident thriving. Meaningful work, PsyCap, ASL, and social support all significantly predicted 1 or more outcomes related to resident thriving (burnout, depression, engagement, stay intent) across all 3 waves. Thus, this study provides theoretical and practical implications for future intervention studies and designing current programming for resident thriving.

关于住院医师参与度、抑郁、倦怠和住院意向相关因素的多波研究。
背景:许多研究都记录了住院医师精神疾病的流行情况,但很少有研究关注住院医师的精神健康或指导干预措施的设计,以促进住院医师培训项目的积极变革,支持住院医师的茁壮成长。在工作需求-资源模型(JD-R)和积极心理学的启发下,本研究探讨了住院医师不良情绪(职业倦怠、抑郁)和幸福感(参与度、住院意向)的四个潜在预测因素,这些因素具有可塑性,因此可以通过干预措施加以改变:心理资本(PsyCap)、指导医师的自主-支持型领导风格(ASL)、社会支持和有意义的工作:2017年11月至2018年9月期间,我们在美国一家大型医院系统收集了三波数据。由于参与者的回复率问题,我们无法按计划进行纵向分析。因此,在每一波数据中,我们都使用贝叶斯回归分析来检验 4 个预测因子与每个结果之间的横截面关系:尽管研究结果在 3 个波次中有所不同,但结果基本符合预期。只有一个例外(第 2 波中的抑郁症状),在所有 3 波中,有意义的工作都能以预期的方向显著预测所有结果变量。在所有 3 个波次中,PsyCap 对职业倦怠、抑郁症状和敬业度的预测都与预期方向一致。ASL 对所有 3 个波次的参与度都有明显的预期预测作用,对 2 个波次的抑郁症状和逗留意愿以及 1 个波次的职业倦怠也有明显的预期作用。社会支持对所有 3 个波次的抑郁症状和 1 个波次的职业倦怠有明显的负向预测作用:结论:应用 JD-R 框架和积极心理学视角可以为制定支持住院医师茁壮成长的计划开辟新的途径。在所有 3 个波次中,有意义的工作、PsyCap、ASL 和社会支持都能显著预测一种或多种与住院医师蓬勃发展相关的结果(职业倦怠、抑郁、参与度、住院意愿)。因此,本研究为未来的干预研究和设计当前的住院医师蓬勃发展计划提供了理论和实践意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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