Trajectories of and Risk Factors for University Students’ Emotional Well-Being and Distress Across the Academic Year
Bibliographic record
Abstract
In recent years, post-secondary students’ mental health has become an important public health concern. Guided by the dual-factor model of mental health, this study examined average mental health fluctuations and associations with a comprehensive list of pre-existing risk factors in Canadian undergraduates (N = 1,004, 61% women, 36% visible minority) followed 16 times throughout the 2020/2021 academic year during the COVID-19 pandemic. We used piecewise latent growth curve modelling to specify patterns of emotional well-being (positive affect) and distress (depressive and anxiety symptoms) across the year. We also examined stressful life experience and sociodemographic risk factors as predictors of baseline levels of emotional well-being and distress in September. Mental health declined in the first half of each semester, remained stable until the end of each semester, and improved over the winter break. Mental health history, past and recent stressful life experiences, age, gender, sexual orientation, visible minority status, subjective social status, and current financial strain predicted baseline mental health at the start of the academic year. This study offers novel insights into patterns of change in students’ mental health and associated risks important for campus programming and intervention efforts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".