Perfectionism in Adolescents: Self-Critical Perfectionism as a Predictor of Depressive Symptoms Across the School Year
Bibliographic record
Abstract
Introduction: The present study examined the influence of personal standards and self-critical perfectionism on depressive and anxiety symptoms over the academic year. Methods: High-school students (N = 174) were surveyed in the late Fall and early Spring, assessing perfectionism in the Fall and mental health across the year in both the Fall and Spring. Path modelling was used to examine whether self-critical and personal standards perfectionism were related to changes in mental health across the school year. Results: Controlling for mental health at the start of the year, self-critical perfectionism predicted an increase in depressive symptoms over time, whereas personal standards perfectionism was unrelated to changes in mental health. Discussion: Results support that self-critical perfectionism is detrimental to mental health in adolescents, suggesting that future interventions should focus on reducing self-critical cognitive biases in youth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".