Trajectories of depression and anxiety symptoms over time in the transition to university: Their co-occurrence and the role of self-critical perfectionism
Bibliographic record
Abstract
Abstract Little is known about how mental health symptoms develop during the transition to university. Most anxiety and depression research fails to consider how symptom development differs over time across different individuals, and how symptom co-occurrence influences the severity of mental health problems. Students (N = 658) completed online surveys on mental health prior to starting university and every 2 months until April. To better understand mental health problems during this transitional period, latent class growth curve analyses were run to determine how anxiety and depressive symptoms co-develop over time, as well, if self-critical perfectionism was a transdiagnostic risk factor for more severe symptom trajectories in this transition. About 40% of students experienced depression and anxiety symptoms prior to entering/during the transition to university. There is substantial variation between students in terms of how they experience depression and anxiety symptoms, and research needs to take this heterogeneity into account to properly identify which students might benefit most from resources. Self-critical perfectionism was a transdiagnostic risk factor, such that students higher in this trait experienced more severe anxiety and depressive symptom trajectories during this transition. This research further implicates the importance of understanding and studying individual differences in symptom development.
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".