Attachment avoidance, alexithymia, and gender: Examining their associations with distress disclosure tendencies and event-specific disclosure.
Bibliographic record
Abstract
Distress disclosure has been linked with reduced psychological distress, increased wellbeing, and successful psychotherapeutic outcome. Because of the importance of distress disclosure, researchers have worked to develop and improve theoretical models of disclosure to facilitate counseling practices that reduce impediments to disclosure. Presently, we conducted a 2-part study to investigate distress disclosure's associations with attachment avoidance, gender, and alexithymia-3 constructs frequently linked with disclosure. In Part 1, we examined the extent to which attachment avoidance, alexithymia, and gender predicted general disclosure tendencies. In Part 2, we examined the extent to which attachment avoidance, alexithymia, and gender predicted event-specific disclosure. Participants were recruited from a crowdsourcing website (N = 178 in Part 1; N = 108 in Part 2). In Part 1, alexithymia partially mediated the association between attachment avoidance and disclosure tendencies, and the link between attachment avoidance and alexithymia was stronger for men than women. In Part 2, the association between distress intensity and event-specific disclosure was weaker for people with high levels of alexithymia. Implications for counseling theory and practice are discussed. (PsycINFO Database Record
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".