Veterans behind bars: Examining criminogenic risk factors of Veteran incarceration
Bibliographic record
Abstract
Introduction: Research on former military personnel incarcerated in Canada is limited. The objectives of this study were to determine the characteristics and criminogenic risk factors of Veterans using a convenience sample of 25 inmates at five Ontario detention centres from 2012 to 2015. This study builds on a pilot project of 19 former military personnel incarcerated at three Ontario detention centres between 2011 and 2012. Methods: Data on sociodemographic variables, military service, and history of physical and mental health problems was obtained through semi-structured interviews. Further data was gathered from institutional health care records. The official offence history and Level of Service Inventory–Ontario Revised (LSI–OR) scores of the inmates, if available, were obtained via client profiles. Results: Twenty-five male inmates self-identified as having been in the military and consented to participate in the study. Their mean age was 43.5 years. Participants indicated serving an average of six years in the military. Fifty-two percent of participants served in the Canadian Armed Forces and 24% in the United States Armed Forces. Other countries of service included Cuba, South Korea, former Yugoslavia, Portugal and Venezuela. Seventy-two percent had prior incarcerations, and 44% were convicted of criminal offences during their military service. For those on remand, 29.2% had been charged with homicide and related offences at the time of the study. A total of 48% of participants indicated involvement in war or operational missions during their military service. Seventy-two percent were diagnosed with a mental health condition during their lifetime. Discussion: This study provides valuable information about the unique characteristics, criminogenic risk factors, and mental health needs of incarcerated Veterans. If Veterans are identified on admission to a correctional facility, future care could be more appropriately directed to reduce criminal recidivism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".