Psychosocial Support Programs for the Mental Well-Being of High School Learners From Low- to Middle-Income Countries: Protocol for a Scoping Review
Bibliographic record
Abstract
Background: The World Health Organization reported in 2020 that approximately 50% of all mental health disorders in adolescents manifest before the age of 14 years. However, the literature on mental well-being and programs designed and implemented by nurses for adolescents in low- to middle-income countries (LMICs) is limited. This scoping review explores the development and implementation of psychosocial support programs targeting high school learners in LMICs. Objective: This prospective scoping review will examine the psychosocial support programs that exist and how they have been implemented for high school learners from LMICs. Methods: Using the Joanna Briggs Institute scoping review framework, we will identify primary research articles through systematic searches of the ERIC, MEDLINE, ScienceDirect, PubMed, and PsycInfo databases. Gray literature will also be sourced from Google Scholar. Two independent reviewers will apply the predetermined inclusion criteria to select studies. Data will be charted, analyzed narratively, and presented in tables and figures. Results: This review will analyze the psychosocial support programs for high school learners in LMIC, identifying gaps in the literature and highlighting areas for further investigation, thereby aiding in creating or adjusting such programs. This study is not funded, and data collection was completed in May 2025. Data analysis is currently in progress, and the results are expected to be finalized and submitted for publication in February 2026. Conclusions: This scoping review will synthesize evidence on psychosocial support programs in LMICs and guide the development of targeted interventions to address the mental health needs of high school learners.
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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 | 0.096 | 0.083 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.019 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.066 | 0.014 |
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".