Promoting mental well-being among secondary school students in Vietnam using the Y-MIND app: A protocol for a hybrid type 2, sequence pre-post, quasi-experimental study
Bibliographic record
Abstract
Youth mental health is recognized as an urgent priority worldwide. Life skills education and self-management skills delivered at the population level are evidence-based approaches to promote mental well-being and resilience among youth and digital technologies are identified as promising approaches for delivering these interventions. The study objective is to assess implementation and clinical outcomes of the delivery of a life skills and self-management intervention delivered at the population level via an app (Y-MIND) in Vietnamese secondary schools. Y-MIND was co-designed with Vietnamese youth to promote appropriateness, acceptability and uptake. We will conduct a hybrid type 2, sequence pre-post, quasi-experimental study in twelve schools across three Vietnamese provinces. Participants will be students aged fifteen years who provide assent to participate and receive parental consent. Control and intervention cohorts from participating schools will be enrolled in subsequent years. The primary clinical outcome measure is well-being measured using the Warwick Edinburgh Well-Being Scale. Secondary outcomes will explore factors including resilience, common mental health conditions, positive childhood experiences, academic stress, internet addiction, substance use and experiences of bullying and punishment. Implementation outcomes include app engagement, uptake, retention and qualitative interviews to understand acceptability and appropriateness of the intervention. Assessments will be administered via an online survey using Qualtrics at baseline, six and twelve months. The intervention cohort will receive access to the app, while the control cohort will receive usual care. The control cohort will have access to the app following the study period, but no app-use data will be collected. Generalized Linear Mixed-Effect Models, with the school specified as the clustering variable, will be used for all outcomes data. Understanding the potential for clinical effectiveness and successful implementation of the Y-MIND app will contribute important evidence to the fields of global youth mental health, digital health and implementation science. Trial registration This study is registered at ClinicalTrials.gov ( NCT06753344 ).
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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 | 0.028 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.041 | 0.009 |
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".