Invisible Scars: Uncovering the Psychological Anatomy of Crime Among Former Inmates A Study Conducted in a Reintegration Community Between 2020 and 2023
Bibliographic record
Abstract
This study explores the relationship between psychopathological traits and criminal behavior in a sample of 54 former male inmates living in a Portuguese reintegration community between 2020 and 2023. Using data from the Millon Clinical Multiaxial Inventory (MCMI) and the Mini-Mult, alongside structured clinical risk and protective factors assessments (HCR-20 and SAPROF), we examined associations between psychological profiles, crime type (violent vs. non-violent), and reintegration outcomes. Descriptive and inferential analyses revealed distinct psychopathological profiles across violent and non-violent offenders, T-test results indicated significantly higher scores in the Antisocial and Narcissistic scales among individuals who committed violent crimes. While the Mini-Mult revealed some positive trends, the MCMI-IV demonstrated greater sensitivity in differentiating clinical profiles, related to violence and risk. Furthermore, individuals with greater psychopathological severity exhibited significantly higher HCR-20 scores (p = .006), indicating elevated risk of recidivism, while no significant association was found with protective factors (SAPROF). Psychological intervention duration was also predictive of positive outcomes: inmates who received therapy for more than one year were 3.4 times more likely to successfully reintegrate. Conversely, employment status and family support showed no statistically significant impact on reintegration. These findings underscore the relevance of clinical assessment tools for risk evaluation and highlight the importance of sustained psychological interventions in the rehabilitation process.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".