Larger social networks may increase stigma against vocal illness: An integrated empirical and computational study of deciphering help-seeking behaviors and vocal stigma

Background Individuals with a stigmatized medical condition are often reluctant to seek medical help. Among professionals, singers and actors often experience stigma associated with voice disorders. Scientific evidence for vocal stigma is, however, limited and primarily anecdotal. No quantitative research has explored the impact that vocal stigma may have on help-seeking behavior in professional vocal performers. This study deployed and integrated empirical and computational tools to (1) quantify the experience of vocal stigma and help-seeking behaviors and (2) predict their modulations with peer influences in social networks. Methods Experience of vocal stigma and information-motivation-behavioral (IMB) skills were prospectively profiled using online surveys from a total of 403 Canadians (200 vocal performers and 203 controls). The survey data were used to formulate an agent-based network model that numerically simulates the effect of social interactions on vocal stigma and help-seeking behaviors. Each virtual agent updates their IMB states via social interaction, which in turn changes their self- and social-stigma states. Profiles from vocal performers and non-vocal performers were compared as a function of network size. Network analysis was performed to evaluate the effect of social network structure on the flow of information and motivation among virtual agents. Results Over 4000 simulation runs in each context, larger social networks are more likely to contribute to an increase in vocal stigma. For small social networks, total stigma was reduced with higher total IMB but much less so for large networks with around 400 agents. For the agent population of vocal performers with high social-stigma and risk for voice disorder, their vocal stigma is resistant to large changes in IMB. Agents with extreme IMB and stigma values are also likely to polarize their networks faster in larger social groups. Conclusions We used empirical surveys to contextualize vocal stigma and IMB in real world populations and developed a computational model to theorize and 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 public policy or social interventions of reducing health stigma in voice disorders and other medical conditions.