Associations between Facets of Pathological Personality Traits and Alexithymia: The roles of Detachment and Negative Affect
Bibliographic record
Abstract
Alexithymia is considered to be a transdiagnostic risk factor for psychopathology, including cluster C personality disorder (PD). However, the association of alexithymic traits and pathological personality traits indicative of cluster C PD has not yet been explored. The current study therefore examined (1) whether (and which) underlying facets of Detachment and Negative Affect are associated with the different components of alexithymia, and (2) whether these associations depend on the level of perceived stress. In total, 635 undergraduate students (Mage = 20.02, 87.5% female) filled out online questionnaires on alexithymia (Toronto Alexithymia Scale) and pathological personality traits (Personality Inventory for DSM-V). Two Multivariate Analysis of Variance (MANOVA) were used to test the hypotheses. The models included the three subscales of alexithymia (i.e., difficulty identifying feelings (DIF), difficulty describing feelings (DDF), externally oriented thinking (EOT)) as outcomes and the three underlying facet traits of either Detachment (i.e., withdrawal, anhedonia, intimacy avoidance) or Negative Affect (i.e., emotional lability, anxiousness, separation insecurity) as predictors. After correction for multiple testing, intimacy avoidance was found to be the most consistent predictor of all subscales of alexithymia, while other facets show more specific associations with DIF, DDF, and/or EOT. These findings highlight the importance of the pervasive influence of specific facet traits representing interpersonal difficulties on specific alexithymia subscales. These associations were not dependent on the level of perceived stress. Replication of these findings in clinical samples may help to identify specific targets for intervention in order to bring about long-term positive outcomes for cluster C PDs.
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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 | 0.000 | 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) | 0.001 | 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".