Proximal Predictors of Depressive Symptomatology: Perceived Losses in Self-Worth and Interpersonal Domains and Introjective and Anaclitic Mood States
Bibliographic record
Abstract
Although much research has demonstrated a relationship between negative life events and depressive symptoms, relatively little research has examined the mechanisms that may mediate this relationship. The theories of Blatt (1974), Bowlby (1980), and Gilbert (1992) each propose proximal predictors of depression. In accordance with these theories, this study examined the relationships among perceived losses in self-worth and interpersonal relationships, anaclitic (dependent) and introjective (self-critical) mood states, and depressive symptoms following a significant negative life event. A sample of 172 undergraduate students completed measures of depressive symptoms and depressive vulnerability factors and retrospectively described the worst period of their lives. They also rated the extent to which the events surrounding this worst period affected their self-worth and their relationships with close others. Structural equation modeling demonstrated that the effect of a perceived loss of self-worth on depressive symptoms was fully mediated by both introjective and anaclitic mood states, whereas the effect of a perceived loss of interpersonal relationships on depressive symptoms was fully mediated by an anaclitic mood state. Additionally, perceived losses of self-worth showed a stronger effect on introjective mood in highly self-critical individuals. Findings highlight the importance of perceived losses in both self-worth and interpersonal domains in response to adverse life events and suggest pathways through which perceived losses may affect 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 | 0.001 | 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 | 0.000 | 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) | 0.002 | 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".