The Impact of Mobile Health Interventions on Mental Health Literacy: Protocol for a Systematic Review and Meta‐Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: The increasing prevalence of mental health disorders is a global public health challenge. Mental health literacy is essential for preventing, recognizing, and managing mental disorders. Mobile health (mHealth) platforms, with their accessibility and portability, offer an opportunity to enhance mental health literacy. OBJECTIVE: This protocol outlines the methodology for conducting a systematic review and meta-analysis of randomized controlled trials to quantitatively assess the effectiveness of mHealth platforms in improving mental health literacy. METHODS: A comprehensive search will be conducted across databases, including Medline, Embase, PsycINFO, Web of Science, and CINAHL, using predefined keywords related to "mental health literacy," "mHealth," "mobile health," and "randomized controlled trials." The search will be supplemented by manual searches of reference lists of relevant studies and reviews to identify additional eligible studies. The key inclusion criterion is the restriction of studies to randomized controlled trials assessing mHealth interventions aimed at enhancing mental health literacy. Only studies published in English will be included. The primary outcome will be changes in mental health literacy scores, measured using validated questionnaires. The primary summary measure will be the standardized mean difference in mental health literacy scores between intervention and control groups, with 95% confidence intervals calculated for each effect size. A mixed-effects model will be used to account for variability across studies. Subgroup analyses will examine variations based on participant age, type of mHealth platform, and intervention duration. The Cochrane Risk of Bias tool will be employed to assess study quality. Sensitivity analyses will be conducted to assess the robustness of the findings, and publication bias will be evaluated using funnel plots and Egger's test. CONCLUSIONS: To our knowledge, this is the first systematic review and meta-analysis of randomized controlled trials to evaluate the effectiveness of mHealth platforms in increasing mental health literacy. The findings will inform policy and practice by empirically assessing the role of digital health technology in promoting mental health education, potentially guiding the integration of mHealth interventions into mental health services and shaping future strategies for public health initiatives.
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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 | 0.118 | 0.159 |
| Meta-epidemiology (narrow) | 0.011 | 0.008 |
| Meta-epidemiology (broad) | 0.034 | 0.043 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.085 | 0.010 |
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".