Examining the Link Between Multidimensional Perfectionism and Depression: A Longitudinal Study of the Intervening Effects of Social Disconnection
Bibliographic record
Abstract
Introduction: The Perfectionism Social Disconnection Model (PSDM) posits that perfectionism confers risk for depression by promoting social disconnection. However, the specific indirect effect of social disconnection on the prospective relation of perfectionism dimensions with depression severity is not well understood. The aim of the current study was to provide the first comprehensive examination of the PSDM. Methods: A diverse community sample of 447 completed measures of trait perfectionism, perfectionistic self-presentation styles, and depressive symptoms at baseline. Six months later, participants completed measures of perfectionistic self-presentation styles, social disconnection, and depressive symptoms. Indirect effects models were analyzed to examine the impact of each facet of perfectionism on social disconnection and subsequent depression severity. Results: Consistent with the PSDM, all perfectionism traits and self-presentation styles resulted in greater depression severity via one or more facets of social disconnection, with social hopelessness and loneliness demonstrating the most widespread effects. Furthermore, perfectionistic self-presentation styles and social disconnection demonstrated sequential indirect effects on the relation of self-oriented and socially prescribed perfectionism with depressive symptoms at follow-up. Discussion: This study is the first to demonstrate the depressogenic effects of all perfectionism dimensions. Findings delineate the interpersonal mechanisms underlying the perfectionism-depression link.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".