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

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Fig. 1. Proposed health stigma and discrimination framework for vocal stigma.

Plausible drivers and facilitators contribute to the process of vocal stigmatization. The top of the diagram shows internalized stigma, or self-stigma, and the manifestations and outcomes related to internalizing vocal stigma. The bottom of the diagram outlines how externalized stigma, or social-stigma, manifests and leads to outcomes in a social context. Adapted from ref. .

Fig. 2. Proposed information motivation behavioral skills model of predicting help-seeking behavior for voice disorders.

Adopted from ref. .

Fig. 3. The framework of vocal stigma agent-based models (VS-ABM).

An agent’s vocal health, vocal stigma, information, motivation, and behavioral skills (IMB) are presumably interrelated and modulated via the social and personal feedback loops, which collectively affect an agent’s tendency to seek help.

Fig. 4. Total stigma experience as functions of network size and number of interactions in agents with low, average, and high initial social-stigma.

a Aggregated trends of mean total stigma scores. Each trend aggregated from 100 simulations initialized with social-stigma and network size. b Varying outcomes of mean total stigma score from individual simulations for VP agents with a high risk of voice disorders, high initial social-stigma, and no intervention within the 400-agent network size. The aggregated trend, the same as the VP group in Fig. 4a, for all 100 simulations is shown in bold. c A representative snapshot of simulated social networks with 400-VP agents near the maximal total stigma after 100,000 interactions in the 400-VP network. An agent’s social-stigma is color-coded from low (Green) to high (Red).

Fig. 5. Probability distribution of total IMB and total stigma experience.

A total of 500,000 social interactions were simulated for VP, non-VP, and general populations with low, average, or high initial social-stigma. a Total IMB and its components (information, motivation, and behavioral skills) at 50 and 400 network sizes. b Total stigma and its components (self-stigma and social-stigma) at 50 and 400 network sizes. c Average change in total IMB (diamonds) and total stigma (circles) from their initial values. Each row of subplots represents a specific magnitude of change, i.e., from less than 1 SD (σ) to more than 2 SD increase (inc.) or decrease (dec.), in total IMB and total stigma. % = percentage of simulations with corresponding changes in total stigma across all network sizes. d Ratio of simulations with total stigma changes more than 1 SD (Panel c row 1 + 2) relative to those with little or no changes (Panel c row 3).

Fig. 6. Network structure analysis of network size effects on IMB and vocal stigma.

a Distribution of an average number of peer links (degree of nodes) by network size. b Distribution of average local clustering coefficient by network size. c Total IMB values decrease as a function of an average number of peer links, which is highly correlated with network size. A clear bifurcation appears around 4.5 peer links. d Total stigma values increase as a function of an average number of peer links. No bifurcation appears. However, distributions show a larger range of changing stigma values for more peer links. Note all panels show all 4000 simulations; panels (a, b) plot distribution of all 500 for each network size, while panels (c, d) plot all each simulation results individually for a total n = 4000.