The Differential Influence of Personal Standards and Self-Critical Perfectionism on Mental Health in Students Transitioning to University: A Longitudinal Analysis with Latent Growth Curve Trajectories
Bibliographic record
Abstract
The transition to university can be a stressful time for emerging adults.Perfectionism is a prevalent trait in university populations and has been associated with increased likelihood of mental health problems.A year-long longitudinal study was conducted to examine whether perfectionism negatively influenced mental health in students transitioning to university.Students (N=656) were recruited prior to university and followed up with at three time-points throughout the year (October, January, April).At each time-point participants completed surveys on perfectionism, depression, anxiety, physical symptoms and stress.Using latent growth curve analyses, self-critical perfectionism was found to predispose students to experience more stress, depression, physical symptoms and anxiety before beginning university and consequently throughout the school year.Contrary to our predictions, students higher in self-critical perfectionism reported stable (but not increased) stress and anxiety during the transition to university.Conversely, personal standards perfectionism was found to be related to decreased mental health scores at baseline.Self-critical perfectionism is a factor which predicts poor mental health adjustment in students transitioning to university.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".