ReviewBMC psychiatry2024
Action recommendations review in community-based therapy and depression and anxiety outcomes: a machine learning approach.
Review in BMC psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 13 citations in OpenAlex.
- AI-driven early diagnosis of specific mental disorders: a comprehensive study.Cognitive neurodynamics · 2025Article
- Preprocessing Large-Scale Conversational Datasets: A Framework and Its Application to Behavioral Health Transcripts.JMIR formative research · 2025Article
- Clinical Effects of Asynchronous Provider-Guided Practice Sessions During Blended Care Therapy for Anxiety and Depression: Pragmatic Retrospective Cohort Study.Journal of medical Internet research · 2024Article
- The Artificial Third: A Broad View of the Effects of Introducing Generative Artificial Intelligence on Psychotherapy.JMIR mental health · 2024Article
- Correction: Action recommendations review in community-based therapy and depression and anxiety outcomes: a machine learning approach.BMC psychiatry · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWhile the positive impact of homework completion on symptom alleviation is well-established, the pivotal role of therapists in reviewing these assignments has been under-investigated. This study examined therapists' practice of assigning and reviewing action recommendations in therapy sessions, and how it correlates with patients' depression and anxiety outcomes.
methodsWe analyzed 2,444 therapy sessions from community-based behavioral health programs. Machine learning models and natural language processing techniques were deployed to discern action recommendations and their subsequent reviews. The extent of the review was quantified by measuring the proportion of session dialogues reviewing action recommendations, a metric we refer to as "review percentage". Using Generalized Estimating Equations modeling, we evaluated the correlation between this metric and changes in clients' depression and anxiety scores.
resultsOur models achieved 76% precision in capturing action recommendations and 71.1% in reviewing them. Using these models, we found that therapists typically provided clients with one to eight action recommendations per session to engage in outside therapy. However, only half of the sessions included a review of previously assigned action recommendations. We identified a significant interaction between the initial depression score and the review percentage (p = 0.045). When adjusting for this relationship, the review percentage was positively and significantly associated with a reduction in depression score (p = 0.032). This suggests that more frequent review of action recommendations in therapy relates to greater improvement in depression symptoms. Further analyses highlighted this association for mild depression (p = 0.024), but not for anxiety or moderate to severe depression.
conclusionsAn observed positive association exists between therapists' review of previous sessions' action recommendations and improved treatment outcomes among clients with mild depression, highlighting the possible advantages of consistently revisiting therapeutic homework in real-world therapy settings. Results underscore the importance of developing effective strategies to help therapists maintain continuity between therapy sessions, potentially enhancing the impact of therapy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.