Clinical Psychological Screening and Early Identification of Trauma-Related Disorders Using AI-Supported Assessment Models

Abstract

This study investigated the persistent problem of delayed, inconsistent, and clinically difficult early identification of trauma-related disorders in psychological screening settings, where symptom overlap, hidden distress, and time-constrained assessment often weaken timely recognition and referral decisions. The purpose of the study was to examine whether AI-supported assessment models improve clinical psychological screening and the early identification of trauma-related disorders through stronger predictive capability, reliability, usability, and clinical trust within a quantitative, cross-sectional, case-based research design. The study drew on a purposive sample of 214 mental health professionals from case-based clinical and enterprise-style digital screening environments, including clinical psychologists, counselors, psychiatrists, and mental health practitioners. Key variables included predictive capability, reliability, usability, clinical trust, screening effectiveness, diagnostic confidence, and early identification of trauma-related disorders. Data were collected through a structured five-point Likert-scale questionnaire and analyzed using descriptive statistics, Cronbach’s alpha, Pearson correlation, and regression modeling in SPSS. The findings showed high mean scores across all core constructs, including predictive capability (M = 4.08, SD = 0.61), screening effectiveness (M = 4.11, SD = 0.59), diagnostic confidence (M = 4.03, SD = 0.63), and early identification (M = 4.15, SD = 0.57). Reliability testing confirmed strong internal consistency, with construct alpha values ranging from 0.79 to 0.88 and an overall instrument alpha of 0.91. Correlation results indicated significant positive associations, particularly between screening effectiveness and early identification (r = 0.68, p < .01), and between clinical trust and diagnostic confidence (r = 0.64, p < .01). Regression results showed that predictive capability (β = 0.31, p < .001), reliability (β = 0.24, p = .002), and usability (β = 0.18, p = .011) significantly predicted screening effectiveness, while screening effectiveness strongly predicted early identification (β = 0.54, p < .001); the models explained 57% and 62% of the variance respectively. The study concludes that AI-supported assessment models can substantially strengthen trauma-sensitive early screening when they are accurate, dependable, usable, and trusted, with important implications for digital mental health practice, clinician support, and early intervention planning.

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