Does emotion matter? The role of alexithymia in violent recidivism: A systematic literature review
Bibliographic record
Abstract
BACKGROUND: Several variables have been evidenced for their association with violent reoffending. Resultant interventions have been suggested, yet the rate of recidivism remains high. Alexithymia, characterised by deficits in emotion processing and verbal expression, might interact with these other risk factors to affect outcomes. AIM: Our goal was to examine the role of alexithymia as a possible moderator of risk factors for violent offender recidivism. Our hypothesis was that, albeit with other risk factors, alexithymia increases the risk of violent reoffending. METHOD: We conducted a systematic literature review, using terms for alexithymia and violent offending and their intersection. RESULTS: (a) No study that directly tests the role of alexithymia in conjunction with other potential risk factors for recidivism and actual violent recidivism was uncovered. (b) Primarily alexithymia researchers and primarily researchers into violence have separately found several clinical features in common between aspects of alexithymia and violence, such as impulsivity (total n = 24 studies). (c) Other researchers have established a relationship between alexithymia and both dynamic and static risk factors for violent recidivism (n = 16 studies). CONCLUSION: Alexithymia may be a possible moderator of risk of violent offence recidivism. Supplementing offenders' rehabilitation efforts with assessments of alexithymia may assist in designing individually tailored interventions to promote desistance among violent offenders.
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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".