Psychographic segmentation to identify higher-risk teen peer crowds for health communications: Validation of Virginia's Mindset Lens Survey

Abstract

Audience segmentation is necessary in health communications to ensure equitable resource distribution. Peer crowds, which are macro-level teen subcultures, are effective psychographic segments for health communications because each crowd has unique mindsets, values, norms, and health behavior profiles. These mindsets affect behaviors, and can be used to develop targeted health communication campaigns to reach those in greatest need. Though peer crowd research is plentiful, no existing peer crowd measurement tool has been formally validated. As such, we developed and validated Virginia's Mindset Lens Survey (V-MLS), a mindset-based teen peer crowd segmentation survey to support health communication efforts. Using an online convenience sample of teens (N = 1,113), we assessed convergent and discriminant validity by comparing the V-MLS against an existing, widely-used peer crowd survey (I-Base Survey®) utilizing a multi-trait multi-method matrix. We also examined the V-MLS's predictive ability through a series of regressions using peer crowd scores to predict behaviors, experiences, and traits relevant to health communication campaign planning. The V-MLS demonstrated reliability and convergent and discriminant validity. Additionally, the V-MLS effectively distinguished teen peer crowds with unique health behaviors, experiences, and personal traits. When combined with appropriate information processing and campaign development frameworks, this new tool can complement existing instruments to inform message framing, tone, and style for campaigns that target at-risk teens to increase campaign equity and reach.

The leading global health challenges in the artificial intelligence era

“…With AI advancement, data analysis and segmentation can be done for several characteristics such as behavior, opinion, and attitude. Using these data, the ML can analyze the online health information and provide personalized massaging to influence individuals’ health behaviors with high quality and clarity, amplifying their influence and effectiveness ( 165–167 ). This health communication can also inform AI technology in developing effective communication systems with patients and their healthcare providers.…”