Discrepancies confer vulnerability to depressive symptoms: A three-wave longitudinal study.
Bibliographic record
Abstract
Discrepancies (i.e., a subjective sense of falling short of one's own standards) are a key part of the perfectionism construct. Theory suggests discrepancies confer vulnerability to depressive symptoms. Since most research in this area is cross-sectional, longitudinal research is needed to disentangle directionality of relationships and to permit stronger causal inferences. Determining whether discrepancies are an antecedent of depressive symptoms, a consequence of depressive symptoms, or both is critical to understanding the discrepancies-depressive symptoms relationship. Knowledge about the temporal stability of discrepancies is also only starting to emerge, and it is unclear whether discrepancies predict incremental variance in depressive symptoms above and beyond neuroticism (i.e., a dispositional tendency to experience negative emotional states). The present study tested relationships among discrepancies, neuroticism, and depressive symptoms in 127 1st-year undergraduates using a 3-wave longitudinal design. Results suggest discrepancies may be understood as a trait-state where people are both highly consistent in their rank order on discrepancies and fluctuate somewhat in the level of discrepancies they experience at a particular point in time. As hypothesized, discrepancies predicted increases in depressive symptoms, even after controlling for neuroticism. Contrary to hypotheses, depressive symptoms did not predict changes in discrepancies. This study extends a long tradition of theory noting the depressing consequences of believing that one has fallen short of one's own standards. Harsh self-criticism and unobtainable self-expectations involving a strong sense of imperfection may be part of the premorbid personality of people vulnerable to depressive symptoms.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".