Association Between Care Utilization and Anxiety Outcomes in an On-Demand Mental Health System: Retrospective Observational Study
Bibliographic record
Abstract
BACKGROUND: Anxiety is an extremely prevalent condition, and yet, it has received notably less attention than depression and other mental health conditions from a research, clinical, and public health perspective. The COVID-19 pandemic has only exacerbated growing concerns about the burden of anxiety due to the confluence of physical health risks, economic stressors, social isolation, and general disruption of daily activities. OBJECTIVE: This study examines differences in anxiety outcomes by care modality (coaching, teletherapy and telepsychiatry, and combined care) within an on-demand mental health system. We also explore the association between levels of engagement within each care modality and odds of improvement in symptoms of anxiety. METHODS: We conducted a retrospective observational study of individuals who accessed Ginger, an on-demand mental health system. Data were collected from 1611 Ginger members between January 1, 2018, and December 31, 2019. We used logistic regression to assess the association between care modality and improvement in anxiety symptoms. Within each modality, we assessed the association between level of engagement and improvement. RESULTS: Of 1611 Ginger members, 761 (47.0%) experienced a decrease in anxiety symptoms, as measured by a change from a positive to a negative 2-item Generalized Anxiety Disorder (GAD-2) screen. Among members who still screened positive at follow-up (865/1611, 53%), a total of 192 members (11.9%) experienced a clinically significant score reduction in the full GAD-7 (ie, a score reduction of >5 points), even though their GAD-2 scores were still positive. All modalities showed increased odds of improvement compared to those who were not engaged with coaching or clinical services ("app-only"). Higher GAD-7 intake scores were also associated with decreased odds of improvement. CONCLUSIONS: This study found increased odds of anxiety improvement for all care modalities compared to those who did not engage in care, with larger effect sizes for higher utilization within all care modalities. Additionally, there is a promising observation that those engaged in combined care (teletherapy and text-based coaching) had the greatest odds of anxiety improvement. Future directions include more detailed classifications of utilization patterns and an exploration of explanations and solutions for lower-utilization members.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".