The UNIversity students' LIFEstyle behaviors and Mental health cohort (UNILIFE-M): Study protocol of a multicenter, prospective cohort study
Bibliographic record
Abstract
Abstract Background Students enrolling in higher education often adopt lifestyles linked to worse mental health, potentially contributing to the peak age onset of mental health problems in early adulthood. However, extensive research is limited by focusing on single lifestyle behaviors, including single time points, within limited cultural contexts, and focusing on a limited set of mental health symptoms. Methods The UNIversity students' LIFEstyle behaviors and Mental health cohort (UNILIFE-M) is a prospective worldwide cohort study aiming to investigate the associations between students' lifestyle behaviors and mental health symptoms during their college years. The UNILIFE-M will gather self-reported data through an online survey on mental health symptoms (i.e., depression, anxiety, mania, sleep problems, substance abuse, inattention/hyperactivity, and obsessive/compulsive thoughts/behaviors) and lifestyle behaviors (i.e., diet, physical activity, substance use, stress management, social support, restorative sleep, environment, and sedentary behavior) over 3.5 years. Participants of 69 universities from 28 countries (300 per site) will be assessed at university admission in the 2023 and/or the 2024 academic year and followed up for 1, 2, and 3.5 years. Discussion The study portrays a unique opportunity to comprehensively understand how multiple lifestyle behavior trajectories relate to mental health symptoms in a large international cohort of university students.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".