Understanding Counseling Attrition: Insights from Logistic Regression

This study investigates the predictors of early termination among clients re-ceiving counseling services at a community mental health centre. Early termi-nation, or dropout, poses significant challenges to the efficacy of thera-peutic interventions, often leading to incomplete treatment and unaddressed mental health needs. Understanding the factors that contribute to this phenom-enon is crucial for improving client retention and optimizing therapeutic out-comes. To explore these factors, a sample of clients who had engaged in coun-seling at the mental health centre was selected for the study. Each participant was inter-viewed, and their responses were meticulously recorded to ensure the accuracy and integrity of the data. The interview process was designed to en-courage hon-est disclosure, enabling the collection of reliable information re-garding their experiences and reasons for either continuing or discontinuing therapy. The study employed a binary logistic regression model to analyse the likelihood of early termination among these clients. The dependent variable in the model was the occurrence of early termination, operationalized as a binary outcome where '1' indicated early termination and '0' signified continuation of counseling until the intended completion. The group that did not terminate early was used as the reference category, while those who terminated early rep-resented the target group for analysis. Two independent variables were identi-fied as potential pre-dictors of early termination: symptom severity and avoid-ance of disclosure. Symptom severity ('sympsev') refers to the intensity of the clients' psychologi-cal symptoms, which was hypothesized to influence their likelihood of remain-ing in therapy. Avoidance of disclosure ('avdiscl') measures the extent to which clients were reluctant to share personal information during counseling sessions, which could hinder the therapeutic process and contribute to premature drop-out. Both predictors were treated as continuous variables within the model. The logistic regression analysis aimed to determine the rela-tive contribution of these predictors to the likelihood of early termination. By calculating the odds ratios for each predictor, the study assessed how variations in symptom severity and avoidance of disclosure influenced the probability of a client terminating therapy prematurely. The results of this analysis provide valuable insights into the dynamics of client retention in counseling settings