Predicting dropout and non-response to psychotherapy for personality disorders: A study protocol focusing on therapist, patient, and the therapeutic relationship

Background. The abandonment of psychotherapeutic treatments is influenced by various factors, including patient characteristics, therapist traits, and the therapeutic relationship. Despite the well-documented importance of these factors, limited empirical research has focused on the role of the therapeutic relationship and the characteristics of therapist-patient dyads in predicting treatment dropout. This study protocol outlines a longitudinal research project aimed at predicting dropout and non-response in psychotherapy for individuals with personality disorders. The research seeks to identify predictive factors related to psychotherapy outcomes, focusing on patient, therapist, and dyadic elements. Specifically, the study will examine the influence of therapist characteristics (e.g., personality traits, countertransference, responsiveness) on treatment outcomes, explore the impact of relational factors (e.g., treatment expectations, epistemic trust, therapeutic alliance) on therapy effectiveness, and assess how the therapeutic alliance within therapist-patient dyads affects the likelihood of dropout and non-response. Methods. The longitudinal study will include 100 therapist-patient dyads (200 participants) recruited from various Mental Health Services in Milan, Italy. Validated instruments will be administered to both patients and therapists at four-time points: T0 (baseline), T1 (3 months), T2 (6 months), and T3 (1 year). Data will be collected at baseline and at the one-year mark to evaluate the relationships between therapist, patient, and dyadic factors and treatment outcomes. Discussion. Identifying predictive variables associated with high dropout rates can help preempt treatment discontinuation, reducing the financial and operational burdens on mental health services. Understanding these factors will enable the development of targeted interventions to improve treatment engagement and reduce attrition. This approach could enhance outcomes for individuals with personality disorders and lead to more efficient resource allocation and sustainable delivery of mental health care.