An integrated empirical and computational study to decipher help-seeking behaviors and vocal stigma
Bibliographic record
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".