OBJECTIVE Most research on the dose-effect (DE) and good-enough level (GEL) models of change has used general outcome measures. The purpose of this study was to determine if predictions from… Click to show full abstract
OBJECTIVE Most research on the dose-effect (DE) and good-enough level (GEL) models of change has used general outcome measures. The purpose of this study was to determine if predictions from these models generalize to specific presenting concerns and outcome measures. METHOD A large sample of treatment-seeking college students (N = 64,319) who attended different numbers of therapy sessions and completed the College Counseling Assessment of Psychological Symptoms-34 (CCAPS-34, Locke et al., Measurement & Evaluation in Counseling & Development, 2012, 45, p. 151) during sessions was used. An analysis of reliable and clinically significant improvement (RCSI) and latent growth curve models (LGCMs) were used for clients attending different numbers of sessions across eight scales from the CCAPS-34 to examine the: (a) amount of change from the first to last session, (b) rates of RCSI, (c) shape of change trajectories, and (d) rates of change across sessions. RESULTS Across all CCAPS-34 scales, clients who attended more sessions tended to experience more improvement, had higher rates of RCSI, and the trajectories of change were nonlinear, consistent with the DE model. Clients who attended fewer sessions tended to experience faster rates of change than those who attended more sessions, consistent with the GEL model. CONCLUSIONS Aspects of both the DE and GEL models appear to generalize to specific outcome measures on the CCAPS-34. Results suggest both individual differences in sensitivity to therapy and amount of therapy received influence therapeutic change. A greater focus on individual needs, especially early in treatment, may be especially important when determining the length of therapy. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
               
Click one of the above tabs to view related content.