Evaluation of ReachOut.com, an Unstructured Digital Youth Mental Health Intervention: Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Young people experience a disproportionate burden associated with mental illness that Australia's mental health care system is ill-equipped to handle. Despite improvements in the provision of mental health services, the rates of service utilization among young people remain suboptimal, and there are still considerable barriers to seeking help. Digital mental health services can overcome a number of barriers and connect young people requiring support; however, the evidence base of digital interventions is limited. OBJECTIVE: The aim of this study is to examine the effectiveness of a brief, self-directed, unstructured digital intervention, ReachOut.com (hereafter ReachOut), in reducing depression, anxiety, stress, and risk of suicide. METHODS: A cohort of 1982 ReachOut users participated in a 12-week longitudinal study, with a retention rate of 81.18% (1609/1982) across the duration of the study. Participants completed web-based surveys, with outcome measures of mental health status and suicide risk assessed at 3 time points across the study period. RESULTS: The results demonstrated that over the 12-week study period, young people using ReachOut experienced modest yet significant reductions in symptoms of depression, anxiety, and stress. Significant, albeit modest, reductions in the proportion of participants at high risk of suicide were also observed. CONCLUSIONS: The findings of this research provide preliminary evidence of the promise of an unstructured digital mental health intervention, ReachOut, in alleviating symptoms of mental ill-health and promoting well-being in young people. These findings are particularly important given that digital services are not only acceptable and accessible but also have the potential to cater to the diverse mental health needs of young people at scale, in a way that other services cannot.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.002 | 0.001 |
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".