MISAPSY: Childhood Maltreatment, Food Insecurity, Psychological Distress and Professional Integration Among Socioeconomically Disadvantaged Young Adults

Overview

The MISAPSY study (Childhood Maltreatment, Food Insecurity, Psychological Distress and Professional Integration Among Socioeconomically Disadvantaged Young Adults) aims to model the complex interrelations between child maltreatment, trauma exposure, food insecurity, and psychological distress among precarious young adults enrolled in French youth employment and social integration services (Mission Locale). Adopting a methodology structured around three complementary components, this study consists of: (1) a cross-sectional survey to identify key associations ; (2) a qualitative study based on semi-structured interviews exploring psychologists' subjective experiences, and (3) a longitudinal comparative interventional study involving two different support programs to assess and compare the impact of these interventions on participants' food insecurity and psychological well-being. Using a multi-phase design, MISAPSY seeks to move beyond linear risk-factor models by adopting a systemic and network-based approach to mental health and social vulnerability. The study integrates quantitative analyses, including network analyses, qualitative exploration of professional practices, and comparative longitudinal intervention to inform more holistic, equitable, and transferable models of care for vulnerable young adults.

The MISAPSY study (Childhood Maltreatment, Food Insecurity, Psychological Distress and Professional Integration Among Socioeconomically disadvantaged Young Adults) is a mixed-methods research project designed to deepen the understanding of the interactions between child maltreatment, trauma exposure, food insecurity and psychological distress among precarious young adults enrolled in French youth employment and social integration services (Mission Locale). This population faces cumulative psychosocial vulnerabilities and remains underrepresented in mental health research. The study addresses this gap by combining quantitative, qualitative, and interventional approaches within a unified and theoretically grounded framework. The study aims to (1) identify key associations and symptom dynamics, (2) explore professional practices and representations of care, and (3) evaluate the effectiveness of integrated support interventions targeting both material and psychological needs. The study is structured around three complementary research components: Cross-sectional study: Collection of socio-demographic data and standardized questionnaires to assess child maltreatment, food insecurity, mental health status, resilience, defense mechanisms, substance use, and perceived social support. The data will be analyzed using descriptive and multivariate statistical techniques (R software). Network analyses will be performed. Qualitative study: Semi-structured interviews will be conducted with psychologists employed at the Mission Locale (Local Youth Employment Center) to support young people. An analysis based on Consensual Qualitative Research will explore individual narratives and better understand the challenges related to the support provided to young people at the Mission Locale. The analyses will be performed using NVivo software. Longitudinal comparative interventional study: comparison of two care programs. The control group receives weekly food assistance for 12 months. The experimental group receives an intervention combining weekly food assistance for 12 months and twice-monthly psychological follow-up for 6 months. Psychological sessions are delivered by trained psychodynamic psychologists who are partners of the Mission Locale. Assessments are conducted at 4 timepoints: T0: baseline, T1: 3 months after baseline, T2: 6 months after baseline (end of the psychological intervention phase), and T3: 12 months after baseline (primary endpoint). It will be conducted combining standardized questionnaires and semi-structured interviews to assess the effectiveness of those interventions on participants' psychological distress and food insecurity. The persistence of the therapy's effects (6 months after the end of follow-up) is being investigated. Data will be analyzed using mixed models and appropriate adjustments for multiple comparisons to assess intervention effectiveness. By integrating network analysis with a mixed-methods framework, MISAPSY moves beyond linear risk-factor models to capture the dynamic and systemic nature of psychological distress and food insecurity among precarious young adults. Registry procedures and quality factors include a Quality Assurance Plan (QAP) ensuring data validation, site monitoring, and ethical compliance through adherence to the Committee for the Protection of Persons (CPP) Ile-de-France XI and both Ethics Committees of University of Lorraine and University of Paris Cité; Data Checks \& Source Data Verification through automated validation rules; a Data Dictionary defining all variables, including sources, coding systems (e.g., ICD-10), and reference ranges; and Standard Operating Procedures (SOPs) will guide participant recruitment, data collection, data management, adverse event reporting, and change management to ensure consistency and compliance. The sample size assessment calls for at least 384 participants for the cross-sectional study to ensure statistical power, 15 participants for the qualitative interviews to achieve data saturation, and 35 participants per group for the longitudinal study (70 in total), with repeated measurements over 12 months to track changes. The plan for managing missing data incorporates multiple imputation techniques, sensitivity analyses, and reviews of data inconsistencies. The statistical analysis plan includes descriptive analyses for the mean, median, and standard deviations, as well as inferential analyses using logistic regression to identify risk factors, mixed-effects models for intervention outcomes, network analyses to observe the dynamics between symptoms, and thematic coding for the qualitative data analyzed with NVivo software.