Integration of Digital Tools Into Community Mental Health Care Settings That Serve Young People: Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Digital mental health tools have substantial potential to be easily integrated into people's lives and fundamentally impact public health. Such tools can extend the reach and maximize the impact of mental health interventions. Before implementing digital tools in new settings, it is critical to understand what is important to organizations and individuals who will implement and use these tools. Given that young people are highly familiar with technology and many mental health concerns emerge in childhood and adolescence, it is especially crucial to understand how digital tools can be integrated into settings that serve young people. OBJECTIVE: This study aims to learn about considerations and perspectives of community behavioral health care providers on incorporating digital tools into their clinical care for children and adolescents. METHODS: Data were analyzed from 5 focus groups conducted with clinicians (n=37) who work with young people at a large community service organization in the United States. This organization provides care to more than 27,000 people annually, most of whom are of low socioeconomic status. The transcripts were coded using thematic analysis. RESULTS: Clinicians first provided insight into the digital tools they were currently using in their treatment sessions with young people, such as web-based videos and mood-tracking apps. They explained that their main goals in using these tools were to help young people build skills, facilitate learning, and monitor symptoms. Benefits were expressed, such as engagement of adolescents in treatment, along with potential challenges (eg, accessibility and limited content) and developmental considerations (eg, digital devices getting taken away as punishment). Clinicians discussed their desire for a centralized digital platform that securely connects the clinician, young person, and caregivers. Finally, they offered several considerations for integrating digital tools into mental health care, such as setting up expectations with clients and the importance of human support. CONCLUSIONS: Young people have unique considerations related to complex accessibility patterns and technology expectations that may not be observed when adults are the intended users of mental health technologies. Therefore, these findings provide critical insights to inform the development of future tools, specifically regarding connectivity, conditional restraints (eg, devices taken away as punishment and school restrictions), expectations of users from different generations, and the blended nature in which digital tools can support young people.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".