Alexithymia among Perpetrators of Violent Offences in Australia: Implications for Rehabilitation
Bibliographic record
Abstract
ObjectiveAlexithymia, which involves difficulties identifying, communicating, and thinking about emotions, could be an important factor in violent offending. Our aim with the current study was to explore the levels of alexithymia among perpetrators of different types of violence (i.e., general and intimate partner) in Australia to better understand their treatment needs.MethodSeventy‐nine male general violent offenders incarcerated in Western Australian prisons, 31 male intimate partner violence (IPV) perpetrators from IPV intervention programs, and 80 men from the general community completed the 20‐item Toronto Alexithymia Scale (TAS‐20).ResultsGeneral violent offenders and IPV perpetrators both scored significantly higher than men from the general community on total alexithymia score and the subscales that measure difficulty identifying and describing feelings; the violent groups did not differ from the general community on externally oriented thinking style. There was no significant difference between the general violent offenders and IPV perpetrators on the total alexithymia score or any of the three subscales of the TAS‐20.ConclusionsThe results of this study suggest that perpetrators of violence in Australia have higher levels of alexithymia than non‐offending men, and that alexithymia should be assessed in the treatment of violent offenders. Our findings also suggest both types of violent offenders have similar alexithymia profiles and that both have difficulties identifying and describing their emotions.
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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 | 0.000 | 0.000 |
| 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".