The Alongside Digital Wellness Program for Youth: Longitudinal Pre-Post Outcomes Study
Bibliographic record
Abstract
BACKGROUND: Youth are increasingly experiencing psychological distress. Schools are ideal settings for disseminating mental health support, but they are often insufficiently resourced to do so. Digital mental health tools represent a unique avenue to address this gap. The Alongside digital program is one such tool, intended as a universal prevention and early intervention. The platform includes social-emotional learning and self-help wellness features as well as an artificial intelligence-powered chatbot designed to build coping skills. OBJECTIVE: This evaluation aimed to examine the near-term impact of Alongside app use on students' self-reported mental health outcomes. METHODS: We conducted a nonrandomized pilot pragmatic evaluation leveraging anonymized user data. All data came from current Alongside users attending public middle and high schools in Texas and New Mexico, between 10 and 18 years old. Schools were actively engaged in partnership with Alongside and approved all procedures. Users were asked to complete mental health questionnaires upon app registration and at 1 and 3 months post registration. We conducted preregistered analyses as well as exploratory analyses to determine how symptoms changed over time and what factors (eg, demographic and app use) predicted changes in distress. RESULTS: =2.21, P=.03, r=0.34); however, there was no evidence that scores significantly decreased from baseline to 3 months (W=1821, n=85, P=.16). We found that at 3 months, identifying as part of the lesbian, gay, bisexual, transgender, queer, and questioning community predicted greater decreases in distress; otherwise, no demographic factors were significant predictors. In a nonregistered exploratory analysis of a subsample of users who reported elevated distress at baseline, decreases in distress were seen at both 1 month (W=128, n=20, P=.02, r=0.52) and 3 months (W=682, n=42, P=.004, r=0.45). CONCLUSIONS: There may be short-term benefits associated with using the Alongside digital program. Further studies are required to determine potential preventative effects.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".