Understanding the influence of early life challenges on alexithymia: A comparative study of offenders and a community sample
Bibliographic record
Abstract
BACKGROUND: Individuals who experience adverse childhood experiences (ACEs) are likely to display alexithymia and are more prone to engage in criminal behaviors. OBJECTIVES: This study aims to assess the relationship between ACEs and alexithymia, to compare a sample of offenders with a community sample in ACEs and alexithymia, and to analyze the predictors of alexithymia. METHODS: The sample comprised 540 participants of both sexes, with 405 individuals from the community and 135 incarcerated individuals. The participants responded to the sociodemographic questionnaire, the Adverse Childhood Experiences Questionnaire (ACEs), and the Toronto Alexithymia Scale (TAS-20). RESULTS: Both samples revealed positive correlations between ACEs and alexithymia. Offenders revealed significantly higher scores of ACEs and alexithymia compared to the community sample. Additionally, emotional neglect in childhood is a significant predictor of alexithymia. CONCLUSION: The findings emphasize the importance of developing prevention strategies to reduce the prevalence of ACEs and alexithymia and decrease their adverse consequences. Further research must be conducted to understand better the intricate interactions between 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".