Predicting future clinical adjustment from treatment outcome and process variables.

Abstract

General issues relating to the use of outcome and process data from the treatment of antisocial children to predict future childhood adjustment are examined. For outcome measures, it was assumed that variables based on direct observation of child behavior would provide a better predictor of long-term adjustment than would ratings by participant adults. Long-term adjustment measures consisted of police arrest and out-of-home placement data collected 2 years after treatment termination. Observation data collected at termination predicted future police arrest, but parent and teacher ratings did not. It was also hypothesized that measures of the processes thought to produce the changes in child antisocial behavior would serve as predictors of future adjustment. The data supported this hypothesis. How do researchers measure change brought about by interventions designed to alter children's antisocial behavior problems? In keeping with the current Zeitgeist, we assumed that the most generalizable procedure would be based on multipleagent, multiple-method assessments (Bank, Dishion, Skinner, & Patterson, 1990). Combining this with modern techniques for analyzing change (e.g., Collins & Horn, 1991) should provide an elegant approach to the problem of evaluating treatment outcome. The necessary groundwork for creating such constructs is missing, however. From this perspective, the need for information relating to both short-term and long-term clinical outcomes becomes apparent. Each treatment should specify its expected long-term clinical outcomes, which may become one means of validating the short-term outcome measures. In addition to studying outcome, treatment programs must assess the theoretical models underlying the intervention. This calls for evaluating the processes hypothesized as causal mechanisms. To the extent that the theoretical model is well specified and assessed, changes in the process variables would also predict the long-term clinical outcomes. Thus, measures of adjustment, process, or both may serve as predictors of long-term outcomes. This article provides a detailed examination of treat

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