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The dosage model provides a normative estimate of the overall pattern of patient improvement in psychotherapy. The phase model further specifies patterns of change in the domains of subjective well-being, symptom remediation, and functioning. The expected treatment response (ETR) approach uses patient characteristics to predict an expected path of progress for each patient. With repeated measures of mental health status, the treatment progress of an individual patient can be assessed against the patient's ETR to support decisions that would enhance the quality of a clinical service while it is being delivered.

Patient profiling predicts improvement across psychotherapy sessions on the basis of intake clinical characteristics. Patients completed questionnaires before their 1st session and at other points during treatment. After using hierarchical linear modeling to calculate expected courses of improvement, 1/2 of the sample was divided into 1 of 2 groups: those whose treatment responses across sessions matched or exceeded their expected courses (75% of the sample) and those whose responses failed to match expectations. A discriminant function analysis indicated that the groups could be differentiated on the basis of intake characteristics; that is, patients with higher discriminant scores were more likely than those with low discriminant scores to have responded to psychotherapy as predicted. Successful cross-validation of the predictor weights was performed with the other 1/2 of the sample.

In the analysis of the impact of clinical interventions, the received wisdom has been that posttreatment scores, with pretreatment scores equated by random assignment or statistically partialed out, should be used to evaluate treatment outcomes. However, posttreatment scores are not generally more reliable than, nor equivalent to, change scores, even with pretreatment scores partialed out of both. Moreover, there are data-analytic methods that indicate how individual patients change, in terms of response curves over time, rather than indicate only how much groups change on the average. These methods take researchers back to the individual data that they ought to use for choosing the specific models of change to be used. To maximize relevance for clinical practice, the results of treatment research should always be reported at this most disaggregated or individual change level, as well as, when appropriate, at more aggregated statistical levels.

Using the psychotherapy dosage model in which effect was probability of recovery, this study compared treatment response rates for psychological symptoms. Symptom checklists were administered to 854 psychotherapy outpatients at intake and during treatment. Sixty-two symptoms were grouped into 3 classes on the basis of probit analysis results. Chronic distress symptoms demonstrated the fastest average response rate, whereas characterological symptoms demonstrated the slowest. Acute distress symptoms showed the highest average percentage of patients recovered across doses. A typical outpatient needed about a year of psychotherapy to have a 75% chance of symptomatic recovery. The model holds promise for establishing guidelines for the financing of psychotherapy.

Mental health services research aims ultimately to improve the quality, impact, and cost-effectiveness of services. Experience during the past decade suggests that the goals and study conditions of mental health services research require special methods. This special section presents 7 articles that give a flavor of the issues addressed and some of the methods that characterize this emerging research area. Although the series cannot explicate the full breadth or all the nuances of these methods, our goal is to entice colleagues to join us in developing methods for addressing services research questions.

The caseload of practicing clinicians tends to be unrepresentative of the population of psychotherapy patients. This results from the fact that, although the majority of patients use relatively few treatment sessions, the majority of a clinician's time is spent with longer term cases--a minority of patients consume the majority of services. Here, a stochastic model is used to describe the development of caseloads under 4 different treatment regimens. It is shown that a psychotherapy practice will reach a steady state (a stable case mix) in relatively short time and at that this will limit the open appointment slots available each week to serve new patients. Implications for training and clinic staffing are discussed.

A 3-phase model of psychotherapy outcome is proposed that entails progressive improvement of subjectively experienced well-being, reduction in symptomatology, and enhancement of life functioning. The model also predicts that movement into a later phase of treatment depends on whether progress has been made in an earlier phase. Thus, clinical improvement in subjective well-being potentiates symptomatic improvement, and clinical reduction in symptomatic distress potentiates life-functioning improvement. A large sample of psychotherapy patients provided self-reports of subjective well-being, symptomatic distress, and life functioning before beginning individual psychotherapy and after Sessions 2, 4, and 17 when possible. Changes in well-being, symptomatic distress, and life functioning means over this period were consistent with the 3-phase model. Measures of patient status on these 3 variables were converted into dichotomous improvement-nonimprovement scores between intake and each of Sessions 2, 4, and 17. An analysis of 2 x 2 cross-classification tables generated from these dichotomous measures suggested that improvement in well-being precedes and is a probabilistically necessary condition for reduction in symptomatic distress and that symptomatic improvement precedes and is a probabilistically necessary condition for improvement in life functioning.