Are Perfectionistic Thoughts an Antecedent or a Consequence of Depressive Symptoms? A Cross-Lagged Analysis of the Perfectionism Cognitions Inventory
Bibliographic record
Abstract
Perfectionistic automatic thoughts have been linked with depressive symptoms in numerous cross-sectional studies, but this link has not been assessed in longitudinal research. An investigation with two timepoints was conducted to test whether perfectionistic automatic thoughts, as assessed by the Perfectionism Cognitions Inventory (PCI), are contributors to subsequent depression or vice versa. The possible role of a third factor (major life events stress) was also evaluated. A sample of 118 university students completed the PCI, the Center for Epidemiologic Studies Depression Scale (CES-D), and the Life Experiences Survey on two occasions with a 5-month interval. A cross-lagged analysis using structural equation modeling showed that above and beyond within-time associations and across-time stability effects, perfectionism automatic thoughts contributed to subsequent depressive symptoms and not vice versa. Negative life events stress was correlated significantly with both depressive symptoms and perfectionism automatic thoughts but did not have an influence on Time 2 depressive symptoms or on perfectionistic automatic thoughts. Our discussion focuses on perfectionistic automatic thoughts as a contributor to depressive vulnerability according to the perfectionism cognition theory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".