Being alexithymic: Necessity or convenience. Negative emotionality × avoidant coping interactions and alexithymia
Bibliographic record
Abstract
OBJECTIVES: We aimed to clarify the associations between negative emotionality, avoidant coping, and alexithymia. We hypothesized that negative emotionality and avoidance strategies would interact negatively in associating with alexithymia. DESIGN: We examined, in one study conducted in Italy and another in the US (total N = 415), the associations among avoidant coping, negative emotionality, and alexithymia, using cross-sectional designs. METHOD: Study 1: Participants completed paper-and-pencil measures of alexithymia, avoidant coping, and negative emotionality. Study 2: Participants completed the above-mentioned measures plus a measure of experiential avoidance (EA), by means of an online questionnaire. RESULTS: As expected, an antagonistic avoidant coping × negative emotionality interaction was found to relate to alexithymia in both studies. In Study 2, EA mediated the effects of such interaction on alexithymia (mediated moderation). The interaction found implied that alexithymia would be adopted as a defence against negative affect or as a consequence of avoidant strategies. CONCLUSIONS: The studies suggested that two different psychological pathways to alexithymia may be at work: Preference for avoidance and negative emotionality. This result appeared theoretically relevant and may stimulate further research. PRACTITIONER POINTS: Alexithymia may develop from habitual avoidance, regardless of negative emotionality. Practitioners could consider addressing negative emotional regulation or automatic and habitual avoidant responses in dealing with alexithymic patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".