An integrated empirical and computational study to decipher help-seeking behaviors and vocal stigma

IF 5.4 Q1 MEDICINE, RESEARCH & EXPERIMENTAL Communications medicine Pub Date : 2024-11-09 DOI:10.1038/s43856-024-00651-3
Aaron R. Glick, Colin Jones, Lisa Martignetti, Lisa Blanchette, Theresa Tova, Allen Henderson, Marc D. Pell, Nicole Y. K. Li-Jessen
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Abstract

Professional voice users often experience stigma associated with voice disorders and are reluctant to seek medical help. This study deployed empirical and computational tools to (1) quantify the experience of vocal stigma and help-seeking behaviors in performers; and (2) predict their modulations with peer influences in social networks. Experience of vocal stigma and information-motivation-behavioral (IMB) skills were prospectively profiled using online surveys from a total of 403 Canadians (200 singers and actors and 203 controls). Data were used to formulate an agent-based network model of social interactions on vocal stigma (self-stigma and social-stigma) and help-seeking behaviors. Network analysis was performed to evaluate the effect of social network structure on the flow of IMB among virtual agents. Larger social networks are more likely to contribute to an increase in vocal stigma. For small social networks, total stigma is reduced with higher total IMB but not much so for large networks. For agents with high social-stigma and risk for voice disorder, their vocal stigma is resistant to large changes in IMB ( > 2 standard deviations). Agents with extreme IMB and stigma values are likely to polarize their networks faster in larger social groups. We integrated empirical surveys and computational techniques to contextualize vocal stigma and IMB beyond theory and to quantify the interaction among stigma, health-seeking behavior and influence of social interactions. This work establishes an effective, predictable experimental platform to provide scientific evidence in developing interventions to reduce health stigma in voice disorders and other medical conditions. Voice professionals such as singers and actors can experience stigma if they have a voice disorder. This stigma can result from their personal experience and knowledge (internalized) or be based on input from their peers, employment, and healthcare providers (externalized). To understand how negative vocal stigma spreads, we surveyed the stigma experience of voice professionals and developed computational models. We find that people tend to have more polarized stigma experiences when they are in larger social groups. Vocal stigma is not changed by a person’s knowledge, beliefs, and tendency to seek help. Our method could be used to study other stigmatized health conditions. Our research could also be used to reduce stigma and promote more equitable health care for vocal professionals with a voice disorder. Glick et al. investigate the stigma experience and help-seeking behavior in professional singers and actors using de novo data and social simulation. They find that vocal performers experience greater discrimination against their vocal injury with simulation data also predicting that vocal stigma could be worsened with larger social groups.

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通过实证和计算综合研究,解读求助行为和声音耻辱感
职业嗓音使用者经常会遭遇与嗓音疾病相关的耻辱,并且不愿寻求医疗帮助。本研究利用实证和计算工具:(1) 量化表演者的嗓音污名化体验和求助行为;(2) 预测社交网络中同伴影响对这些体验和行为的调节作用。这项研究利用在线调查对 403 名加拿大人(200 名歌手和演员以及 203 名对照组)的声带成见经历和信息激励行为(IMB)技能进行了前瞻性分析。数据被用于建立一个基于代理的网络模型,该模型用于分析声带成见(自我成见和社会成见)与求助行为之间的社会互动。通过网络分析,评估了社会网络结构对虚拟代理之间 IMB 流动的影响。较大的社交网络更有可能导致声誉成见的增加。对于小型社交网络而言,总鄙视度会随着总 IMB 的增加而降低,但对于大型网络而言,情况并非如此。对于具有较高社会污名和嗓音障碍风险的代理人来说,他们的嗓音污名对 IMB 的大幅变化(2 个标准差)具有抵抗力。在较大的社会群体中,具有极端 IMB 值和污名值的代理人可能会更快地极化其网络。我们将实证调查和计算技术结合起来,对声誉成见和 IMB 进行了理论之外的背景分析,并量化了成见、健康寻求行为和社会互动影响之间的相互作用。这项工作建立了一个有效、可预测的实验平台,为制定干预措施提供科学依据,以减少嗓音疾病和其他病症的健康成见。歌手和演员等嗓音专业人士如果患有嗓音疾病,可能会遭受成见。这种成见可能来自他们的个人经历和知识(内化),也可能来自他们的同行、就业和医疗服务提供者的意见(外化)。为了了解负面嗓音成见是如何传播的,我们对嗓音专业人员的成见经历进行了调查,并开发了计算模型。我们发现,当人们处于较大的社会群体中时,往往会有更多两极分化的鄙视经历。一个人的知识、信仰和求助倾向并不会改变声带烙印。我们的方法可用于研究其他被污名化的健康状况。我们的研究还可用于减少耻辱感,促进嗓音疾病患者获得更公平的医疗保健。Glick 等人利用新数据和社会模拟研究了专业歌手和演员的成见经历和求助行为。他们发现,声乐表演者在声带损伤方面受到的歧视更大,模拟数据还预测,声带成见可能会随着社会群体的扩大而加剧。
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