Predictors of mental health and academic outcomes in first-year university students: Identifying prevention and early-intervention targets
Bibliographic record
Abstract
BACKGROUND: Although there is growing interest in mental health problems in university students there is limited understanding of the scope of need and determinants to inform intervention efforts. AIMS: To longitudinally examine the extent and persistence of mental health symptoms and the importance of psychosocial and lifestyle factors for student mental health and academic outcomes. METHOD: Undergraduates at a Canadian university were invited to complete electronic surveys at entry and completion of their first year. The baseline survey measured important distal and proximal risk factors and the follow-up assessed mental health and well-being. Surveys were linked to academic grades. Multivariable models of risk factors and mental health and academic outcomes were fit and adjusted for confounders. RESULTS: In 1530 students surveyed at entry to university 28% and 33% screened positive for clinically significant depressive and anxiety symptoms respectively, which increased to 36% and 39% at the completion of first year. Over the academic year, 14% of students reported suicidal thoughts and 1.6% suicide attempts. Moreover, there was persistence and overlap in these mental health outcomes. Modifiable psychosocial and lifestyle factors at entry were associated with positive screens for mental health outcomes at completion of first year, while anxiety and depressive symptoms were associated with lower grades and university well-being. CONCLUSIONS: Clinically significant mental health symptoms are common and persistent among first-year university students and have a negative impact on academic performance and well-being. A comprehensive mental health strategy that includes a whole university approach to prevention and targeted early-intervention measures and associated research is justified.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".