The Tripartite-Structural Interface: Evaluating Mutual Benefits of the Core Emotion Framework and the Dose-Content-Disposition Model in Digital Mental Health

This paper proposes a unified theoretical architecture that integrates the Dose Content Disposition (DCD) model of digital media effects with the Core Emotion Framework (CEF), a structural constructivist model of human affective processing. The DCD framework resolves the long standing “screen time binary” by demonstrating that digital mental health outcomes emerge from nonlinear interactions between exposure patterns, content typology, and user disposition. The CEF complements this ecological taxonomy by providing a mathematically explicit, somatic computational engine capable of decomposing digital experiences into operator level emotional state vectors. Drawing on detailed mappings between DCD dimensions and CEF operators, the paper shows how social comparison, fear based content, restorative environments, and mastery oriented interfaces can be translated into precise 50 facet activation profiles. The integration further formalizes user vulnerabilities—such as neuroticism, ADHD, autism spectrum differences, and depressive collapse—into calibration parameters (TS 8) that can be used to design adaptive, algorithmically responsive interventions. Finally, the paper outlines a three phase Open Science validation roadmap, including psychometric factor analysis, structural equation modeling of operator fusions, and randomized controlled trials of CEF based digital interventions. By bridging macro ecological digital media research with micro mechanistic affective computation, this synthesis reframes digital well being as a predictive, intervene able engineering discipline rather than a descriptive social science. Keywords Core Emotion Framework (CEF); Dose Content Disposition (DCD) Model; digital mental health; structural constructivist affective science; operator vectors; somatic calibration; algorithmic literacy; neurodiversity profiles; psychometric validation; digital well being interventions; computational affect modeling; ecological digital exposure taxonomy.