Understanding interpersonal guilt: Associations with attachment, altruism, and personality pathology
Bibliographic record
Abstract
The aim of this article is to empirically investigate the relationships among interpersonal guilt, as conceived within control-mastery theory (CMT), and attachment, altruism, and personality pathology in an English-speaking sample. An online sample of 393 participants was recruited to complete the Interpersonal Guilt Rating Scale self-report version-15 (IGRS-15s), together with other empirically validated measures for the assessment of attachment, altruism, and personality pathology. On the basis of previous studies conducted in Italian-speaking samples, we hypothesized that survivor guilt, separation/disloyalty guilt, and omnipotent responsibility guilt would be associated with attachment anxiety and avoidance, altruism, and personality pathology; self-hate was hypothesized to be associated only with attachment anxiety and avoidance and personality pathology. Analyses examined bivariate associations as well as the network of partial correlations among variables. The results largely confirmed hypothesized associations, with self-hate evincing the strongest unique association with personality dysfunction. Findings provide a basis for further research regarding interpersonal guilt and personality and relational functioning, with potential implications for clinical conceptualizations of the role of guilt in psychopathology.
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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 | 0.007 |
| 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 | 0.001 |
| 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".