Center Effects: Counseling Center Variables as Predictors of Psychotherapy Outcomes

Treatment context may have a unique impact on psychotherapy outcomes, above and beyond client, therapist, and therapy process variables. University counseling centers represent one such treatment context facing increasing treatment demands. This study examined the role of counseling centers and center variables in explaining differences in psychotherapy outcomes. The Center for Collegiate Mental Health, a large practice–research network, contained data from 116 counseling centers, 2,362 therapists, and 58,423 clients. Multilevel modeling tested if some counseling centers systematically achieved better outcomes than others (a “center effect”). Outcome was operationalized as clients’ magnitude and rate of change in distress across treatment. Results showed a relatively small “center effect” for both outcomes. Analyses sought to explain that center effect through administrative policies and characteristics. As a group, these variables partially explained the center effect. None explained a large portion of total outcome variance. Potential future implications for policy and advocacy efforts are discussed.

Common Factors in Psychotherapy

Common factors, or characteristics that are present across psychotherapies, have long been considered important to fostering positive psychotherapy outcomes. The contextual model offers an overarching theoretical framework for how common factors facilitate therapeutic change. Specifically, this model posits that improvements occur through three primary pathways: (a) the real relationship, (b) expectations, and (c) specific ingredients. The most-well-studied common factors, which also are described within the contextual model, include the therapeutic alliance, therapist empathy, positive regard, genuineness, and client expectations. Empirical studies have demonstrated that a strong therapeutic alliance, higher ratings of therapist empathy, positive regard, genuineness, and more favorable outcome expectations are related to improved treatment outcomes. Yet, the long-standing debate continues regarding whether psychotherapy outcomes are most heavily determined by these common factors or by factors specific to the type of therapy used. There have been calls for an integration of the two perspectives and a shift toward evaluating mechanisms as a way to move the field forward. Nonetheless, the common factors are valuable in treatment delivery and should be a focus in delivering psychotherapy.

Deliberate Practice Makes Perfect: Improving Therapist Effectiveness

Psychotherapy works. The results of numerous studies show that those individuals treated are better off than those not treated or on waitlists with an average effect size of .8 (Duncan et al., 2008). To put it in perspective, the effects of psychotherapy are equal to those found for coronary artery bypass surgery and 4 times greater than fluoride in the prevention dental cavities. Yet, three persistent problems plague the psychotherapy field: 1) clients drop out of therapy at alarming rates – almost half of clients decide not to continue and prematurely terminate; 2) not only do therapists not notice when clients are at risk for dropping out, they also do not detect when things are getting worse (approximately 10% of clients get worse after starting therapy); and 3) a small percentage of clients (10%) accounts for the largest amount of expenditures (Minami, 2008). This last finding may be the result of therapists not realizing when things are not working or getting worse and instead of changing course, doing more of what is not working, over and over again. Along these lines, most therapists do not have an accurate sense of their helpfulness and on average, overrate their effectiveness by 65% (Chow, 2014). Given the issues with retention, coupled with the self-assessment bias among therapists, it’s not surprising that psychotherapy outcomes have not appreciably improved over the past 40 years.

Influencing Factors and Machine Learning-Based Prediction of Side Effects in Psychotherapy

Background: Side effects in psychotherapy are a common phenomenon, but due to insufficient understanding of the relevant predictors of side effects in psychotherapy, many psychotherapists or clinicians fail to identify and manage these side effects. The purpose of this study was to predict whether clients or patients would experience side effects in psychotherapy by machine learning and to analyze the related influencing factors.Methods: A self-compiled “Psychotherapy Side Effects Questionnaire (PSEQ)” was delivered online by a WeChat official account. Three hundred and seventy participants were included in the cross-sectional analysis. Psychotherapy outcomes were classified as participants with side effects and without side effects. A number of features were selected to distinguish participants with different psychotherapy outcomes. Six machine learning-based algorithms were then chosen and trained by our dataset to build outcome prediction classifiers.Results: Our study showed that: (1) the most common side effects were negative emotions in psychotherapy, such as anxiety, tension, sadness, and anger, etc. (24.6%, 91/370); (2) the mental state of the psychotherapist, as perceived by the participant during psychotherapy, was the most relevant feature to predict whether clients would experience side effects in psychotherapy; (3) a Random Forest-based machine learning classifier offered the best prediction performance of the psychotherapy outcomes, with an F1-score of 0.797 and an AUC value of 0.804. These numbers indicate a high prediction performance, which allowed our approach to be used in practice.Conclusions: Our Random Forest-based machine learning classifier could accurately predict the possible outcome of a client in psychotherapy. Our study sheds light on the influencing factors of the side effects of psychotherapy and could help psychotherapists better predict the outcomes of psychotherapy.