A Digital Mental Health Solution to Improve Social, Emotional, and Learning Skills for Youth: Protocol for an Efficacy and Usability Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has exposed a devastating youth mental health crisis in the United States, characterized by an all-time high prevalence of youth mental illness. This crisis is exacerbated by limited access to mental health services and the reduction of mental health support in schools. Mobile health platforms offer a promising avenue for delivering tailored and on-demand mental health care. OBJECTIVE: To address the lack of youth mental health services, we created the Science Technology Engineering Math and Social and Emotional Learning (STEMSEL) study. Our aim was to investigate the efficacy of a digital mental health intervention, Neolth, in enhancing social and emotional well-being, reducing academic stress, and increasing mental health literacy and life skills among adolescents. METHODS: The STEMSEL study will involve the implementation and evaluation of Neolth across 4 distinct phases. In phase 1, a comprehensive needs assessment will be conducted across 3 diverse schools, each using a range of teaching methods, including in-person, digital, and hybrid modalities. Following this, in phase 2, school administrators and teachers undergo intensive training sessions on Neolth's functionalities and intervention processes as well as understand barriers and facilitators of implementing a digital mental health program at their respective schools. Phase 3 involves recruiting middle and high school students aged 11-18 years from the participating schools, with parental consent and student assent obtained, to access Neolth. Students will then be prompted to complete an intake questionnaire, enabling the customization of available modules to address their specific needs. Finally, phase 4 will include a year-long pre- and posttest pilot study to rigorously evaluate the usability and effectiveness of Neolth in addressing the mental health concerns of students across the selected schools. RESULTS: Phase 1 was successfully completed in August 2022, revealing significant deficits in mental health resources within the participating schools. The needs assessment identified critical gaps in available mental health support services. We are currently recruiting a diverse group of middle and high school students to participate in the study. The study's completion is scheduled for 2024, with data expected to provide insights into the real-world use of Neolth among the adolescent population. It is designed to deliver findings regarding the intervention's efficacy in addressing the mental health needs of students. CONCLUSIONS: The STEMSEL study plays a crucial role in assessing the feasibility and adoption of digital mental health interventions within the school-aged youth population in the United States. The findings generated from this study have the potential to dismantle obstacles to accessing mental health assistance and broaden the availability of care through evidence-based strategies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/59372.
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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 | 0.035 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.057 | 0.012 |
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".