Implementing a Peer Advocate Mental Health Digital Intervention Program for Ohio Youth: Descriptive Pilot Study
Bibliographic record
Abstract
BACKGROUND: In the United States, millions of adolescents report poor mental health, where 1 in 5 teenagers considers suicide. Reducing stigma and fostering peer support remains critical for positive mental health interventions and programs. Increasingly, digital mental health tools have emerged with great promise, leveraging social networks. Despite the potential, limited understanding of such comprehensive programs and their implementation exist. OBJECTIVE: The objective of this study investigates a piloted digital mental health training program (Be Present) for youth, specifically describing the impact on youth behavioral outcomes and user engagement and identifying high-risk youth in the early phases of prevention. METHODS: Eligibility included Ohio residents (aged 14 to 22 years) to be enrolled as either a Friend or a Peer Advocate. From May 1 to June 1, 2019, participants completed the Advocate training course, taking pretest and posttest surveys. Single-arm descriptive analyses measured youth outcomes (self-efficacy, intentions, behaviors, social support, knowledge, and sources of strength) and engagement and assessed risk based on survey responses. RESULTS: A total of 65 adolescents participated, with 54 completing both pretest and posttest surveys. The majority of participants included non-Hispanic White females. Findings illustrated a significant increase in self-report of referrals for mental health services as well as in perceptions that youth had of experiencing social support; however, no significant differences were found for measures of self-efficacy, knowledge, and sources of strength between pretest and posttest surveys. Roughly two-thirds of the participants completed all of the Advocate training modules, and we observed a gradual decline in engagement. Most respondents who received escalated high-risk response messages identified as female. CONCLUSIONS: The pilot presented promise for implementing a digital mental health program focused on peer support, specifically observing reported youth behavioral outcomes and user engagement and identifying high-risk youth. Various limitations exist given the small nonrepresentative sample and lack of control group. All findings should be considered preliminary to a larger trial and underscore the feasibility of delivering online training programs to bolster adolescent mental health. Such formative evaluation proved critical for future implementation and research, offering opportunity for substantial improvements for real-world digital mental health programs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".