PROTOCOL: Mobile apps to reduce depressive symptoms and alcohol use in youth: A systematic review and meta‐analysis
Bibliographic record
Abstract
Background: Depressive symptoms and alcohol use in youth doubled in the first year of the COVID-19 pandemic. The COVID-19 pandemic has created sustained disruption in society, schools, and universities, including increasing poverty and discrimination. Public health restrictions have caused isolation and reduced social and emotional support. Together, these factors make depressive symptoms and alcohol use in youth a global public health emergency. Mobile applications (apps) have emerged as potentially scalable intervention to reduce depressive symptoms and alcohol use in youth that could meet increased demands for mental health resources. Mobile apps may potentially reduce psychological distress with accessible technology-based mental health resources. Objectives: This systematic review and meta-analysis aims to assess the effect of mobile apps on depressive symptoms and alcohol use in youth. Search Methods: We will develop a systematic search strategy in collaboration with an experienced librarian. We will search a series of databases (MEDLINE, Embase, PsycINFO, CINAHL, CENTRAL) from January 2008 to July 2021. Selection Criteria: Following the PRISMA reporting guidelines for systematic reviews, two independent reviewers will identify eligible studies: randomized controlled trials on mobile apps for the management of depressive disorders (depression and anxiety) and alcohol use in youth aged 15-24 years of age. Data Collection and Analysis: Eligible studies will be assessed for risk of bias, and outcomes pooled, when appropriate, for meta-analysis. Heterogeneity, if present, will be examined for gender. ethnicity, and socioeconomic status contributions. A narrative synthesis will highlight similarities and differences between the included studies. We will report GRADE summary of finding tables.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.029 | 0.004 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".