Does feedback improve psychotherapy outcomes compared to treatment-as-usual for adults and youth?

Dyason, Katelyn M.; Shanley, Dianne C.; O’Donovan, Analise; Low‐Choy, Samantha

doi:10.1080/10503307.2019.1620367

Cited by 7 publications

(13 citation statements)

References 44 publications

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“…Previous research with this sample has demonstrated that outcomes were improved compared with published treatment-as-usual (i.e., no feedback) benchmarks and similar to published benchmarks from meta-analyses of feedback-informed treatment (Dyason et al, 2020). Thus, it appeared psychotherapists were effectively using the OQ-Analyst feedback to boost outcomes, and this was broadly consistent with findings from other settings using feedback to inform treatment.…”

Section: Limitations and Future Researchsupporting

confidence: 79%

“…Importantly, the creation of ETR for feedback purposes assumes that (a) most clients’ symptoms change in a similar way, so their trajectories can be averaged, and (b) an individual can be meaningfully compared to an average derived from many clients. First, findings from the sudden gains and growth mixture modeling literatures in psychotherapy suggest that there is a great deal of clinically meaningful heterogeneity in client’s responses to psychotherapy, giving limited empirical support to the notion of one typical response to treatment (Dyason, 2019, Chapter 3). Second, comparing an individual to a group-based average could invoke the ecological fallacy (Piantadosi et al, 1988), where serious errors of inference occur when there is considerable variation, mixed effects or confounding variables operating within the group.…”

Section: Current Feedback Procedures: Comparing An Individual To An A...mentioning

confidence: 99%

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Modeling individualized trajectories of symptom change to improve feedback procedures in psychotherapy.

Dyason¹,

Low‐Choy²,

O’Donovan³

et al. 2021

Journal of Consulting and Clinical Psychology

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Objective: Psychotherapy feedback compares an individual's treatment progress to the averaged progress of all clients to determine whether their progress is sufficient. However, this can invoke the ecological fallacy if the average trajectory combines heterogenous trajectories with clinically meaningful differences. The current study, instead, explored individualized trajectories of change in psychotherapy and examined the feasibility of using these individualized models to predict clients' future trajectories. Method: The Outcome Questionnaire-45 was completed at each session of psychotherapy by 398 adults (16 -83 years; M age ϭ 36.01 years) attending two Australian psychology training clinics. Up to seven Bayesian, polynomial curve-linear regression models were fit and compared for each client. For a hold-out sample (N ϭ 50), models were fit sequentially for each client in five-session increments and used to generate tailored predictions of expected progress at the next five sessions. Results: Constant (no change) and linear (steady change) were the most common shapes of change; only 3% of clients experienced negatively accelerating improvement, as per the expected treatment response curve used in current feedback procedures. Three exemplars demonstrated how individualized modeling and predictions could be utilized in clinical practice to provide richer, more nuanced feedback to psychotherapists about client progress and likely prognosis. Conclusions: This study was the first to model individualized trajectories of symptom change across psychotherapy and in doing so, uncovered substantial heterogeneity in client trajectories. This means that averaged trajectories are likely to be misleading. Individualized modeling could complement current feedback procedures. What is the public health significance of this article?Understanding how and when change occurs in psychotherapy can help psychotherapists generate hypotheses about the mechanisms of change for each client. Individualized modeling of symptom change over the course of psychotherapy can complement current feedback procedures by generating highly specific information about each client's progress, allowing psychotherapists to better understand and predict their clients' responses to psychotherapy.

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Section: Limitations and Future Researchsupporting

confidence: 79%

Section: Current Feedback Procedures: Comparing An Individual To An A...mentioning

confidence: 99%

Modeling individualized trajectories of symptom change to improve feedback procedures in psychotherapy.

Dyason¹,

Low‐Choy²,

O’Donovan³

et al. 2021

Journal of Consulting and Clinical Psychology

Self Cite

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“…On the one hand, feedback in the communication process has traditionally been a subject of psychological studies for solving the problem of optimizing communication in various areas of human life and activity [11][12][13][14][15][16]; on the other hand, there is a need to study more deeply the use of feedback in the process of staff management.…”

Section: Methodsmentioning

confidence: 99%

“…On the one hand, there is enough experimental evidence accumulated in modern science regarding the positive effects that emotional intelligence as well as effective feedback has on management success in organizations, on the development of students', prospective consulting psychologists' and psychotherapists' professional skills [11][12][13][14][15][16][17][18][19][20][21][22][23]. It is commonly believed that a leader sets the dominant tone for an entire organization in dealing with their own and other people's emotions, which improves the interpersonal relationships in a team, the quality of work environment and employees' social skills [1][2][3][4][5][6][7][8][9][10][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Innovative aspects of feedback delivery in management process by applying emotional intelligence

Rastorgueva

2021

SHS Web Conf.

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This article focuses on examining new specific practical recommendations for delivering feedback in the process of staff management in organizations by applying the resources of emotional intelligence. Managers’ I-statement mistakes, imperceptible at first sight, are analyzed and explained from the point of view of their psychological correctness; ways of correcting them are presented. In detail, with real-life examples of the feedback in the organizations, it is explained how a leader can take into account the psychological difficulties of feedback perception and increase its effectiveness. Thus, the following mistakes of the feedback are analyzed: the feeling the one giving feedback is not mentioned at all; his feeling is generalized; it is determined in an unspecified way or expressed to a person in general; the person’s behavior, which evokes a feeling, is not described at all or is described in an unspecified way; it is not specified what exactly has caused a feeling in a communicative situation; a conclusion on the person’s actions and the person in general as a generalization of his actions; exaggerating; identifying a trait of character of someone’s actions as a generalization of his actions; emphasis; describing internal processes of another person; expressing another person’s attitude to something or someone; a subjective evaluation; interpreting a person’s actions; an instruction (lecturing, sermonizing); a tirade; a mockery; an accusation; comparing a person with someone; a condemnation; a threat, an insult, a humiliating statement; condescending phrases; a wish; a request. Correct I-statements are a universal mechanism of self-regulation, self-support.

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“…We are still uncertain about how well clinicians can learn from experience, training, and education (Bacon 2019). We also lack evidence of the best way to provide feedback to enhance that learning (Bickman 2008a;Dyason et al 2020). The problem of treatment precision is also currently tied to having the clinician deliver the treatment.…”

Section: Ai and The Future Of Mental Health Servicesmentioning

confidence: 99%

Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health

Bickman

2020

Adm Policy Ment Health

111

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This conceptual paper describes the current state of mental health services, identifies critical problems, and suggests how to solve them. I focus on the potential contributions of artificial intelligence and precision mental health to improving mental health services. Toward that end, I draw upon my own research, which has changed over the last half century, to highlight the need to transform the way we conduct mental health services research. I identify exemplars from the emerging literature on artificial intelligence and precision approaches to treatment in which there is an attempt to personalize or fit the treatment to the client in order to produce more effective interventions. Keywords Mental health services • Artificial intelligence • Machine learning • Precision mental health • Randomized clinical trials (RCTs) • Precision medicine "AI will bring many wonders. It may also destabilize everything from nuclear détente to human friendships. We need to think much harder about how to adapt... it is changing human knowledge, perception and reality-and, in so doing, changing the course of human history. We seek to understand it and its consequences and encourage others across the disciplines to do the same."-Kissinger et al. 2019, pp. 24-26 "AI is one of the most profound things we're working on as humanity. It's more profound than fire or electricity."-Alphabet CEO Sundar Pichai, in Thompson and Bodoni 2020. This article is part of a special issue, Festschrift for Leonard Bickman: The Future of Children's Mental Health Services.

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Does feedback improve psychotherapy outcomes compared to treatment-as-usual for adults and youth?

Cited by 7 publications

References 44 publications

Modeling individualized trajectories of symptom change to improve feedback procedures in psychotherapy.

Modeling individualized trajectories of symptom change to improve feedback procedures in psychotherapy.

Innovative aspects of feedback delivery in management process by applying emotional intelligence

Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health

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