There has been an accumulating weight of evidence that at least for depression, all psychotherapy approaches are similarly effective, including non-directive, supportive counselling (Cuijpers et al., 2013; Pybis, Saxon, Hill & Barkham, 2017). On the other hand, most clinicians have no doubt had the experience of helping a depressed client with the very same approach that didn’t seem to help another, or that different clients achieved their improvement via different treatment plans. It has been persuasively argued that previous randomised controlled trials are statistically underpowered to detect these client-treatment interactions. The latest would-be assassins of the Dodo verdict are looking to cutting-edge machine-learning statistical approaches with large, real-world data sets to see whether they can identify a set of client variables that would predict which psychotherapeutic approach would be optimal for any given person.

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If you’re reading this, chances are that you’re aware I’m running a study on using supervision as an intervention to improve client outcomes. Part of the inspiration for this study came from a systematic review of evaluations of supervision in psychotherapy that Alfonsson and colleagues (2018) completed. They could find only one randomised controlled trial that demonstrated an effect of supervision on therapist competence and no studies that showed an effect on client outcomes. I then developed a structured approach to supervision and am currently testing this at the moment. I still need therapist participants so please, if you know anyone who might be interested, let them know. This week, these same authors (Alfonsson et al., 2020) published the results of their efforts to develop a structured CBT supervision program and evaluate its effects on therapist competence.

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