Psychological approaches to mental health rely primarily on a medical model of assessment, diagnosis, and treatment, which reacts to symptom presentation, affect, and behavior. This approach is limited in that it neither demonstrates a reduction in the rates of mental illness nor prevents future mental health problems in those previously treated. A significant 11-year gap exists between the onset of symptoms and intervention in the current model (National Alliance on Mental Illness, 2021). Furthermore, individuals with preventable physical illnesses are more likely to experience mental health problems, and evidence for treating multimorbidities is limited, compounding the issues of a single-disease response model (Barnett et al., 2012). A comprehensive thematic analysis of research on wellbeing for the application of preventive mental health practices could not be identified, signaling a critical gap. This paper introduces The 5-Elements of Systemic Wellbeing Framework, a conceptual model developed from a reflexive thematic analysis of over 2000 research studies and expert interviews. The analysis identified five core themes of wellbeing (Secure, Regulated, Valued, Decided, Related), each supported by evidence from 36 or more credible resources. Within each theme, twelve subfactors with the strongest evidence were organized into two subcategories: six internal (personal) and six external (environmental) factors. This structure provides a comprehensive, evidence-informed definition of wellbeing designed to serve as a launch point for the systemic prevention of mental health conditions and the promotion of wellbeing as a proactive solution. This is presented as a conceptual framework with limitations in that its application and outcomes are yet to be empirically examined on a large scale.

The prevailing reactive, medical model of mental healthcare is insufficient to address the rising prevalence of mental illness and the significant social and economic costs associated with it. A paradigm shift toward proactive, systemic prevention is urgently needed. This paper introduces the EQUIP model (Examine, Quantify, Unite, Inquire, Plan), a socioecological framework for applied preventive mental health. Grounded in principles of public health, systems theory, and positive psychology, EQUIP provides a structured, iterative process for communities and organizations to move from reactive treatment to proactive wellbeing promotion. The model begins by examining social and structural vulnerabilities (Examine), followed by measuring population wellbeing using the evidence-based 5-Elements of Systemic Wellbeing Framework (Quantify). It then guides the formation of a diverse stakeholder task force (Unite), facilitates a deep, appreciative inquiry into local context and strengths (Inquire), and culminates in a scaffolded, participatory action plan (Plan). The EQUIP model offers a scalable, data-driven, and human-centered approach to architecting environments that buffer stress, build resilience, and foster the conditions for communities to thrive. The systems evaluation and change process is conducted within a fully automated software tool, the MindArchHealth Automation Pathway (MAP).

Wilches, N., Dominguez, G., & Khan, M. (2025). Wellbeing Framework Study: Protective Factors and Mental Health. This study explored mental health through a preventative lens, using the 5-Elements of Systemic Wellbeing Framework (secure, regulated, valued, decided, related). Two consecutive surveys with 60 questions were administered to 424 participants recruited via online platforms, flyers, and peer networks. The results highlighted "Decided" and "Related" as strong elements, while "Regulated" and "Secure" were areas of vulnerability. The study also found potential disparities across gender, race, and childhood income. The key predictive goal, "Increase Security By Improving Equity," suggests that a sense of security and fair treatment are crucial for mental wellbeing. The findings underscore the importance of proactive interventions and addressing systemic barriers to promote mental health.

Integrating data analytics into mental health programming could be beneficial for enhancing outcomes on a population-wide scale. Through implementing data analytics, organizations can acquire insights that guide targeted interventions, which result in improved mental health outcomes for entire communities.

Mental health challenges are quite common among college/university students, with the majority of students meeting the criteria for at least one mental-health issue. The daily psychological stress they experience often results in both mental and physical health problems. Further, mental health can lead to depression, suicidal ideation, or suicide. Therefore organizational change related to mental health can be implemented in schools to help prevent students from poor mental health. 

Colleges/universities could benefit from using predictive analytics in mental health prevention. Predictive analytics is a quintessential type of advanced analysis that uses past data in conjunction with statistical modeling, data mining, and machine learning, to predict future outcomes. Predictive analytics is crucial for mental health prevention especially considering the recent wave of mental health crises on college campuses. 

We are excited to announce the official launch of our MAP software tool, helping you predict and prevent mental health disorders population-wide.

Check out our press release. 9/6/2024
MindArch Health Inc. Launches MAP Version 1.0.0