The Impact of Perfectionism on the Incidence of Major Depression in Chinese Medical Freshmen: From a 1-Year Longitudinal Study
Bibliographic record
Abstract
Background: Perfectionism is a pivotal factor in the etiology and prognosis of major depression. Nevertheless, there is a scarcity of longitudinal research examining the association between perfectionism and major depressive disorder (MDD). The objective of this study was to explore the impact of perfectionism on MDD among a cohort of first-year Chinese university students. Methods: This study employed a longitudinal design to investigate the relationship between perfectionism and MDD in a sample of first-year Chinese university students (n=8079). Socially prescribed perfectionism and almost perfectionism were measured using the Multidimensional Perfectionism Scale (MPS) and the Almost Perfect Scale-Revised (APS-R), while MDD was assessed using the Composite International Diagnostic Interview (CIDI-3.0). Random effects logistic regression modeling was utilized to estimate the associations between the variables. Results: The findings revealed that the incidence of MDD was 0.6%. Lifetime exposure to severe traumatic events (≥10) (OR=2.619, 95% CI: 1.502-4.565) and almost perfectionism (OR=1.015, 95% CI: 1.004-1.026) were identified as significant risk factors for MDD. Conclusion: It is evident that perfectionism is linked to an increased susceptibility to MDD. However, additional longitudinal studies focusing on university students are imperative to delve deeper into the influence of perfectionism on the initial manifestation of MDD.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".