Depression and Anxiety Outcomes in a Technology-Enabled Psychotherapy Practice: Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Mental health conditions account for significant distress, burden, and societal costs. Despite efforts to implement evidence-based practices (EBPs), access to high-quality mental health treatment in general practice remains limited and clinical outcomes suboptimal. Measurement-based care (MBC) is a transtheoretical and transdiagnostic strategy that has the potential, when implemented effectively, to improve the quality of care. Digital tools can also support clinicians by alleviating administrative tasks and providing in-the-moment performance data and clinical decision support. OBJECTIVE: This study aimed to examine patient retention in care and depression and anxiety outcomes in a technology-enabled psychotherapy practice where clinicians were supported by a suite of innovations, including an MBC platform and clinical decision support tools. METHODS: This retrospective cohort study examined 2984 adults who initiated mental health treatment with Two Chairs, a hybrid technology-enabled behavioral health provider, from January 1 to June 30, 2024. Rates of reliable change, recovery, remission, deterioration, and the magnitude and trajectory of symptom change in depression and anxiety symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9) and General Anxiety Disorder-7 (GAD-7). RESULTS: The participants demonstrated high rates of retention in care (3183/3572, 89.1%) and MBC survey completion (3440/3572, 96.3%). From baseline to the 12th session, patients showed significant symptom improvements in depression and anxiety, achieving high rates of reliable improvement (65.8%, 95% CI 64.0%-67.5%) and recovery (53.2%, 95% CI 51.4%-55.0%), with 71.3% (95% CI 69.7%-72.9%) of patients achieving either outcome. Outcomes continued to improve up to the point of completion of the treatment episode (79.2%, 95% CI 77.5%-80.9%, of patients achieving improvement or recovery). Pre- to posttreatment effect sizes were large (all Cohen d>0.9). CONCLUSIONS: The results of this study show that technology-enabled MBC and clinical decision support systems (CDSSs) may support high-quality patient outcomes in mental health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".