Association Between Therapeutic Alliance and Clinical Outcomes in Virtual Telepsychiatry: A Retrospective Analysis of Data from Talkiatry (Preprint)
Bibliographic record
Abstract
BACKGROUND: Given the increasing demand for accessible mental health services, fully virtual telepsychiatry has become a vital component of modern health care delivery. Therapeutic alliance, the collaborative and affective bond between patients and therapists, is a well-established predictor of clinical outcomes in traditional face-to-face and teletherapy. However, the relationship between therapeutic alliance and clinical outcomes in an outpatient telepsychiatry setting remains less understood. OBJECTIVE: Our primary objective was to evaluate the relationship between therapeutic alliance and clinical outcomes for depression and anxiety in an outpatient telepsychiatry practice. METHODS: This retrospective study analyzed data from treatment-seeking adults receiving services from Talkiatry, a single commercial telepsychiatry practice. Treatments included a comprehensive psychiatric evaluation, supportive psychotherapy, and medication management conducted by a psychiatrist utilizing a fully virtual platform. Assessment of anxiety and depressive symptoms using the Generalized Anxiety Disorder scale (GAD-7) and Patient Health Questionnaire scale (PHQ-8), respectively, allowed us to identify patients with at least moderate baseline symptoms (PHQ-8 and/or GAD-7≥10). Patients completed a baseline clinical assessment within 1 week of their first visit. Therapeutic alliance ratings from the patients' perspective were collected using the Working Alliance Inventory-Short Revised (WAI-SR). Follow-up clinical assessments were conducted between 8 and 16 weeks after their first visit. We used regression models to assess whether therapeutic alliance was associated with clinically significant improvement, which was defined as having ≥50% improvement in depression and/or anxiety symptoms at follow-up after adjusting for demographic (age, region, sex, insurance type, urban/rural), baseline clinical score (PHQ-8 or GAD-7), time from baseline to follow-up, and baseline prescription status covariates. RESULTS: After application of the inclusion criteria (PHQ-8 and/or GAD-7≥10), we identified 170 patients for depression analyses and 157 for anxiety analyses. Mean baseline symptom severity scores for PHQ-8 and GAD-7 were 14.90 (SD 3.81) and 14.64 (SD 3.39), respectively. In response to telepsychiatry treatment, patients showed a 41.1% reduction in anxiety symptoms from baseline (d=1.16, P<.001) and a 38.8% reduction in depressive symptoms (d = 1.07, P<.001). After controlling for other covariates, higher WAI-SR scores were significantly associated with greater likelihood of clinically significant improvement for both anxiety (OR=1.07, P<.001, 95% CI: [1.03, 1.11]) and depression (OR=1.04, P=.03, 95% CI: [1.01, 1.09]). In addition, receipt of prescription within the seven days of the first visit was significantly associated with a greater likelihood of clinical improvement for depression (OR=4.99, P<.001, 95% CI: [2.10, 13.10]), but not anxiety (OR=1.54, P=.33, 95% CI [.65, 3.69]). CONCLUSIONS: Therapeutic alliance scores were associated with significant clinical improvements in both depression and anxiety, whereas medication status was associated with significant improvements for depression but not anxiety. These findings speak to the importance of fostering strong therapeutic relationships between patients and psychiatrists, even when treatment is delivered virtually.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".