Bibliographic record
Abstract
BACKGROUND: Factors contributing to the development of alexithymia and the nature of alexithymia's relation with trait negative and positive affectivity are unclear. In this study, a twin approach was used to examine the degree of genetic and environmental contributions to the different facets of alexithymia, and the nature of their relations to trait negative and positive affectivity. METHOD: Forty-five monozygotic and 32 same-sex dizygotic twin pairs completed the Toronto Alexithymia Scale-20, the Eysenck Personality Inventory, and a zygosity questionnaire. RESULTS: Model fitting analyses indicated that familial influences contributed significantly to all three facets of alexithymia. Parameter estimates and intraclass correlations suggested, though could not confirm, that it was shared environmental factors that contributed to difficulty identifying and communicating emotions (ID and COM), but shared genetic factors that contributed to externally oriented thinking (EOT). Between-twin cross-trait twin analyses revealed strong correlations between ID and neuroticism, and between COM and extraversion, and suggested that it is shared familial influences which account for these associations. CONCLUSIONS: The results of this study indicate that: (a) the different facets of alexithymia are influenced by familial factors; (b) the previously noted associations between ID and COM and trait affectivity are not merely methodological artifacts; and (c) the associations between ID and COM and trait affectivity are influenced by familial factors. The results also suggest that ID and COM are largely influenced by shared environmental factors, but that EOT is influenced by genetic factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".