Although resilience benefits from social interactions, there is a gap regarding the identification of key players that contribute to it. This study uses social network analysis (SNA) to identify those players based on the modeling of interactions associated with four abilities of resilient systems: monitor, anticipate, respond, and learn. Networks are developed for each ability, and a score is proposed for each player, combining five indicators theoretically connected to resilience: in-degree, closeness, and betweenness, which are derived from SNA, and availability and reliability, which are non-network attributes assessed through Likert-style questions. The proposal was implemented using data on 133 professionals from an intensive care unit. Five semi-structured interviews supported the interpretation of survey data and analysis of contextual factors. The ranking of key players varied across ability-based networks, and none of them excelled in all dimensions of the proposed score. The proposal bridges the gap between the role of individual players in work systems and system resilience. That occurs as the score, while being attributed to individual players, reflects their interactions with others. SNA is thus an effective analytical approach for dealing with the resilience engineering tension between individual performance and context.

尽管韧性受益于社会互动，但在识别对其做出贡献的关键参与者方面存在差距。本研究使用社交网络分析 (SNA) 根据与弹性系统四种能力相关的交互建模来识别这些参与者：监控、预测、响应和学习。为每种能力开发网络，并为每个参与者提出一个分数，结合五个理论上与弹性相关的指标：入度、接近度和介数，它们来自 SNA，可用性和可靠性，它们是非网络属性通过李克特式的问题进行评估。该提案是使用来自重症监护病房的 133 名专业人员的数据实施的。五次半结构化访谈支持调查数据的解释和背景因素的分析。关键参与者的排名在基于能力的网络中各不相同，并且没有一个人在建议分数的所有维度上都表现出色。该提案弥合了个人参与者在工作系统中的作用与系统弹性之间的差距。发生这种情况的原因是分数虽然归因于个人玩家，但反映了他们与他人的互动。因此，SNA 是处理个人表现和环境之间弹性工程张力的有效分析方法。发生这种情况的原因是分数虽然归因于个人玩家，但反映了他们与他人的互动。因此，SNA 是处理个人表现和环境之间弹性工程张力的有效分析方法。发生这种情况的原因是分数虽然归因于个人玩家，但反映了他们与他人的互动。因此，SNA 是处理个人表现和环境之间弹性工程张力的有效分析方法。