Measuring Mental Health in 2 Brazilian University Centers: Protocol for a Cohort Survey
Bibliographic record
Abstract
BACKGROUND: Global concern for the mental well-being of university students is on the rise. Recent studies estimate that around 30% of students experience mental health disorders, and nearly 80% of these individuals do not receive adequate treatment. Brazil, home to around eight million university students, lacks sufficient research addressing their mental health. To address this gap, we aim to conduct a longitudinal mental health survey at 2 Brazilian universities. OBJECTIVE: This paper outlines the research protocol for a web-based mental health survey designed to assess the well-being of Brazilian university students. METHODS: The survey targets undergraduate students (N=8028) from 2 institutions: UniFAJ (Centro Universitário de Jaguariúna) and UniMAX (Centro Universitário Max Planck). Students will be invited to respond to self-reported questionnaires, including theSMILE-U (lifestyle and quality of life), the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition]) self-rated level 1 cross-cutting symptom measure, and a brief version of the Adult Self-Report Scale for attention-deficit/hyperactivity disorder. Students who exceed thresholds for conditions such as depression, anxiety, and attention-deficit/hyperactivity disorder will receive additional diagnostic instruments. The survey will be conducted annually, tracking individual and group trajectories and enrolling new cohorts each year. Data will be analyzed using cross-sectional and longitudinal methods, focusing on descriptive, associative, and trajectory analyses. RESULTS: The first wave of data collection began in February 2024 and is expected to conclude in December 2024. As of October 2024, a total of 2034 of 7455 (27.2 in 100) eligible students had completed the questionnaire. Cross-sectional statistical analysis is planned to commence immediately after data collection and is expected to be completed by June 2025. CONCLUSIONS: This survey uses a scalable, cost-effective design to evaluate mental health conditions among Brazilian university students. The longitudinal framework facilitates the monitoring of mental health trends, supports the development of targeted interventions, and informs policy initiatives in higher education. TRIAL REGISTRATION: OSF Registries OSF.IO/AM5WS; https://doi.org/10.17605/OSF.IO/AM5WS. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/63636.
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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 | 0.030 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".