Outcomes of a Blended Care Coaching Program for Clients Presenting With Moderate Levels of Anxiety and Depression: Pragmatic Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Depression and anxiety are leading causes of disability worldwide, but access to quality mental health care is limited by myriad factors. Cognitive-behavioral coaching is rooted in evidence-based principles and has the potential to address some of these unmet care needs. Harnessing technology to facilitate broader dissemination within a blended care model shows additional promise for overcoming barriers to care. OBJECTIVE: The aim of this study is to evaluate the outcomes of a blended care coaching (BCC) program for clients presenting with moderate levels of anxiety and depression in real-world settings. METHODS: This study examined retrospective data from US-based individuals (N=1496) who presented with moderate levels of depression and anxiety symptoms and who received blended care coaching services. Using a short-term framework, clients met with coaches via a secure video conference platform and also received digital video lessons and exercises. To evaluate the effectiveness of the BCC program, mixed effects modeling was used to examine growth trajectories of anxiety and depression scores over the course of care. RESULTS: Out of the total sample of 1496 clients, 75.9% (n=1136) demonstrated reliable improvement, and 88.6% (n=1326) recovered based on either the Generalized Anxiety Disorder-7 scale (anxiety) or Patient Health Questionnaire-9 (depression). On average, clients exhibited a significant decline in anxiety and depression symptoms during the initial weeks of coaching, with a continued decline over subsequent weeks at a lower rate. Engaging in a coaching session was associated with lower anxiety (b=-1.04) and depression (b=-0.79) symptoms in the same week, as well as lower anxiety (b=-0.74) and depression (b=-0.91) symptoms the following week (P<.001). CONCLUSIONS: The BCC program demonstrated strong outcomes in decreasing symptomology for clients presenting with moderate levels of anxiety and depression. When clients received coaching sessions, significant decreases in symptoms were observed, reflecting the importance of session attendance. Additionally, the steepest declines in symptoms tended to occur during the initial weeks of coaching, emphasizing the importance of client buy-in and early engagement. Collectively, these findings have implications for addressing unmet mental health care needs in a more accessible, cost-effective manner.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".