Childhood matters: How benevolent and adverse childhood experiences shape alexithymia in perpetrators of sex crimes and the community sample
Bibliographic record
Abstract
BACKGROUND: Benevolent childhood experiences (BCEs) and adverse childhood experiences (ACEs) influence individuals' emotional and behavioral development. ACEs can lead to alexithymia, increasing the risk of sexual offending. OBJECTIVES: To analyze the link between ACEs, BCEs, and alexithymia; compare perpetrators of sex crimes with community participants regarding ACEs, BCEs, and alexithymia; and assess the predictors of alexithymia. PARTICIPANTS: A sample of 732 adult males (523 from the community and 209 perpetrators of sex crimes) was used. METHOD: Application of a sociodemographic questionnaire, the Adverse Childhood Experiences Questionnaire (ACEs), the Benevolent Childhood Experiences Scale (BCEs), and the Toronto Alexithymia Scale (TAS). RESULTS: We identified positive correlations between ACEs and TAS, and negative correlations between BCEs and TAS in both samples. Perpetrators of sex crimes show higher levels of ACEs, TAS, difficulty identifying feelings (DIF), and difficulty describing feelings (DDF) compared to the community sample. ACEs and BCEs are predictors of DIF and DDF in both samples. CONCLUSION: The results highlight the significant influence of ACEs and BCEs on alexithymia and the differences between community individuals and perpetrators of sex crimes. It underscores the need for interventions to boost BCEs and reduce ACEs, alexithymia, and criminal behavior.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".