Provider Perspectives on the Use of Mental Health Apps, and the BritePath App in Particular, With Adolescents at Risk for Suicidal Behavior: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Many youth with significant mental health concerns face limited access to mental health services. Digital programs, such as mobile apps designed to address mental health issues, have the potential to expand access to strategies for managing these conditions. However, few mental health apps are specifically designed for youth experiencing severe concerns, such as suicidal ideation. BritePath is a new app developed to enhance communication and interaction between providers and youth at risk for suicidal behavior. OBJECTIVE: This study aims to explore health care providers' opinions and concerns regarding the use of mental health apps for youth at significant risk of suicidal behavior. METHODS: We conducted individual semistructured interviews with 17 providers across 7 states. Interviews were conducted via video, recorded, and transcribed. Codes were developed using a team-based approach, with discrepancies resolved through team discussions. RESULTS: Most providers were aware of mental health apps in general and expressed interest in trying the BritePath app with patients experiencing depression, suicidality, or both. Analyses identified 4 key themes related to mental health apps: (1) almost all providers viewed mental health apps as an adjunct to, rather than a replacement for, psychotherapy visits; (2) most providers were concerned about the cost of apps and youth access to them; (3) providers noted the challenge of maintaining patient engagement with apps over time; and (4) providers were concerned about patient privacy, in terms of both data shared with app developers and data privacy within families. Analyses of providers' opinions specifically about the BritePath app identified 4 additional themes: (1) providers believed that access to safety plans within BritePath could be beneficial for youth at risk for suicidal behavior; (2) providers reported that BritePath's interactive features could enhance communication between providers and youth; (3) providers appreciated BritePath's flexibility and the ability for both youth and providers to tailor its content to individual needs; and (4) providers expressed concerns about integrating BritePath into clinical workflows within health systems. CONCLUSIONS: The use of mental health apps is expanding, yet there is limited understanding of how to effectively integrate these tools into mental health treatment. Providers are increasingly referring patients to mental health apps, and most expressed interest in trying the BritePath app for patients with depression, suicidality, or both. However, providers also identified several concerns, particularly regarding privacy and safety.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".