Shenakht Journal of Psychology & Psychiatry

Introduction: Depression is one of the most prevalent psychiatric disorders, characterized by emotional, cognitive, motivational, and somatic symptoms, and it significantly impairs interpersonal relationships.
Aim: The present study aimed to develop and adapt an interpersonal psychotherapy protocol based on the phenomenological components of Iranian patients with depression.
Method: The present study was conducted using a sequential exploratory mixed-methods design. The study population comprised published scientific documents from 2015 to 2023, patients diagnosed with depression, and mental health professionals in Tehran in the year 2023. The sample included 25 scientific articles, 15 patients with depression who had referred to psychotherapy clinics, and 8 mental health professionals in the qualitative phase, as well as 2 patients with depression in the quantitative phase. All participants were selected using purposive sampling. For assessment purposes, the Structured Clinical Interview, the Beck Depression Inventory-II (1996), and the Barkham Interpersonal Problems Inventory (1996) were utilized. In the qualitative phase, the documents were analyzed through systematic review and the interview transcripts were examined using thematic analysis. In the quantitative phase, data were analyzed using descriptive statistics and Cohen’s d effect size.
Results: The results indicated that the adapted protocol, based on the phenomenological components of Iranian patients with depression, exhibited satisfactory content validity as well as empirical validity. Preliminary evaluations revealed a significant reduction in the severity of depressive symptoms and an improvement in the quality of participants’ interpersonal relationships.
Conclusion: The current results provide evidence for the preliminary efficacy of the adapted protocol in ameliorating depressive symptoms and interpersonal difficulties in Iranian patients with depression. Further research with larger clinical samples is recommended to enhance the validation and generalizability of the results.