Attachment, alexithymia, gender, and emotional disclosure : an interactional investigation
Bibliographic record
Abstract
Emotional disclosure—verbal communication of emotional experiences—reduces emotional distress and positively impacts interpersonal relationships. Consequently, concealing emotions has been shown to negatively impact physical and psychological health. Previous research has shown that people with attachment avoidance orientation, and people with alexithymia, limit their use of emotional disclosure as a means of affect regulation. Little research however, has been conducted to determine if alexithymia mediates the negative relation between attachment avoidance and emotional disclosure. Additionally, there is little research evaluating the moderating effect of gender on the relation between attachment avoidance and alexithymia. Presently, we investigated if alexithymia mediated the negative relation between attachment avoidance and emotional disclosure. Secondarily, we evaluated whether gender moderated the positive relation between attachment avoidance and alexithymia. Participants were Mechanical Turk workers (N = 178) who completed measures of attachment orientation, alexithymia, and generalized emotional disclosure tendencies. Our primary hypothesis was supported: alexithymia partially mediated the relation between attachment avoidance and emotional disclosure. Our secondary hypothesis was also supported: gender moderated the relation between attachment avoidance and alexithymia in that the relation was stronger for male participants compared to female participants. Implications for theory and counselling psychology practice will be discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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".