Effects of childhood adversities on alexithymia features vary between sexes. Results of a prospective population study
Bibliographic record
Abstract
Introduction: Adverse childhood experiences (ACEs) associate with various mental disorders, including personality features. Our understanding of how ACEs influence alexithymia features in the general population is limited. In a prospective population setting, we studied whether ACEs associate with alexithymia, and the role of sex and emotional symptoms in this association.Methods: In a Finnish population-based prospective study, 3,142 individuals aged between 30 and 64 years completed eleven ACE questions and the Toronto Alexithymia Scale in 2000 and 2011, and the Hopkins Symptoms Checklist in 2011. The effect of ACEs on alexithymia and its subdomains – difficulty identifying feelings (DIF), difficulty describing feelings (DDF), and externally oriented thinking (EOT) in 2000 and 2011 – was analysed using repeated measures ANOVA.Results: The number of ACEs and their main component, childhood social disadvantage, associated positively with total alexithymia scores and its subdomains DIF and DDF, and negatively with EOT. After controlling for the effect of depression and anxiety, the strength of these associations was reduced, but the effect of social disadvantage on DIF and EOT remained significant in females. Childhood family conflicts associated positively with DIF in males and negatively with EOT in females. Additionally, maternal mental problems associated positively with DIF and DDF in females.Discussion: In the general population, ACEs, particularly social disadvantage, are associated with adult alexithymia features. Alexithymia features, detectable from youth, may predispose individuals to emotional disturbances caused by childhood adversities. The effect of family conflicts and maternal mental problems on alexithymia features varies between sexes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".