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预测、预防和管理加拿大一线医护人员及领导者在面对新冠疫情时所遭受的道德伤害:一项混合方法研究的方案

Predict, prevent and manage moral injuries in Canadian frontline healthcare workers and leaders facing the COVID-19 pandemic: Protocol of a mixed methods study.

作者信息

Gilbert-Ouimet Mahée, Zahiriharsini Azita, Biron Caroline, Langlois Lyse, Ménard Caroline, Lebel Manon, Pelletier Jérôme, Duchaine Caroline, Beaulieu Marianne, Truchon Manon

机构信息

Department of Health Sciences, Université du Québec à Rimouski, 1595 Boulevard Alphonse-Desjardins, Lévis, QC, G6V 0A6, Canada.

CHU de Québec-Laval University Research Center, 1050 Chemin Ste-Foy, Quebec City, QC, G1S 4L8, Canada.

出版信息

SSM Ment Health. 2022 Dec;2:100124. doi: 10.1016/j.ssmmh.2022.100124. Epub 2022 Jun 1.

Abstract

Moral injuries can occur when perpetrating, failing to prevent, or bearing witness to acts that transgress deeply held moral beliefs and expectations. The COVID-19 crisis highlighted the fact that psychosocial stressors at work, such as high emotional demands, are placing Canadian healthcare workers at risk of moral injuries. Evidence linking psychosocial stressors at work to moral injuries are needed to better predict, prevent and manage moral injuries, as these stressors are frequent and modifiable occupational risk factors. This protocol presents a study aiming to: 1) understand workplace events having the potential to either cause or reduce moral injuries, 2) predict the risk and severity of moral injuries using a disease prevention model, 3) identify biological signatures (biomarkers) associated with psychosocial stressors at work and moral injuries and 4) elaborate preliminary guidelines of organizational practices for frontline healthcare workers to reduce and manage moral injuries. This study is a mixed methods research with three components: qualitative, quantitative and biological. The data collection has been completed and because of the COVID-19 pandemic, it was adjusted to allow for gathering qualitative and quantitative data remotely. Frontline healthcare workers and leaders were included. Through focus groups and individual interviews, and an online questionnaire, events and psychosocial working conditions that may increase the risk of moral injuries will be documented. In addition, blood samples which were collected from a sub-sample of volunteer participants will measure an innovative set of biomarkers associated with vulnerability to stress and mental health. Data analyses are ongoing. We anticipate to identify workplace events that may trigger moral injuries. We expect that potential predictors of moral injury risk occurrence and severity will be identified from psychosocial stressors at work that can be improved by implementing organizational practices. We also expect to observe a different mental health state and biological inflammation signature across workers exposed compared to workers not exposed to psychosocial stressors at work. Based on these future findings, we intend to develop preliminary recommendations of organizational practices for managers. This research will contribute to expand our knowledge of the events in the workplace likely to generate or lessen the impact moral injuries, to build a model for predicting the risk of moral injuries at work, all in the specific context of the COVID-19 health crisis among healthcare workers.

摘要

当实施、未能阻止或目睹违背根深蒂固的道德信仰和期望的行为时,可能会发生道德伤害。新冠疫情突出了这样一个事实,即工作中的心理社会压力源,如高情感需求,正使加拿大医护人员面临道德伤害的风险。需要有证据将工作中的心理社会压力源与道德伤害联系起来,以便更好地预测、预防和管理道德伤害,因为这些压力源是常见且可改变的职业风险因素。本方案提出了一项研究,旨在:1)了解有可能导致或减少道德伤害的工作场所事件;2)使用疾病预防模型预测道德伤害的风险和严重程度;3)识别与工作中的心理社会压力源和道德伤害相关的生物标志物;4)为一线医护人员制定组织实践的初步指南,以减少和管理道德伤害。本研究是一项混合方法研究,包括三个部分:定性、定量和生物学。数据收集已经完成,由于新冠疫情,数据收集方式进行了调整,以便能够远程收集定性和定量数据。研究纳入了一线医护人员和领导。通过焦点小组、个人访谈以及在线问卷,将记录可能增加道德伤害风险的事件和心理社会工作条件。此外,从志愿者参与者子样本中采集的血样将用于测量一组与压力易感性和心理健康相关的创新生物标志物。数据分析正在进行中。我们预计能够识别出可能引发道德伤害的工作场所事件。我们期望从工作中的心理社会压力源中识别出道德伤害风险发生和严重程度的潜在预测因素,通过实施组织实践可以改善这些因素。我们还期望观察到,与未暴露于工作中心理社会压力源的工作人员相比,暴露于这些压力源的工作人员会呈现出不同的心理健康状态和生物炎症特征。基于这些未来的研究结果,我们打算为管理人员制定组织实践的初步建议。这项研究将有助于扩展我们对工作场所中可能产生或减轻道德伤害影响的事件的认识,构建一个预测工作中道德伤害风险的模型,所有这些都是在医护人员面临新冠健康危机的特定背景下进行的。

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