Inequities in Social Determinants of Health Factors and Criminal Behavior: A Case Study of Immigrant Ex-Offenders
Bibliographic record
Abstract
When immigrants arrive in a new country, they often discover that being an immigrant does not allow them to integrate easily into the new society. Immigrant offenders are more likely to engage in criminal behaviors due to inequities in social determinants of health factors as a source of strain. This study was focused on utilizing the personal experiences of immigrant offenders to discover the various circumstances that contributed to their criminal behavior. General Strain Theory has been shown to be a useable theoretical model in explaining the relationship between race/ethnicity and criminal behavior. The participants in this study were adult immigrant ex-offenders in the province of Alberta, Canada. The results of the study indicated a consensus among ex-offenders that there are social determinants of health factors such as stress, income problem, education issues, employment issue, and health risk behaviors that have led them to commit crime. The recommendations presented below are divided into three groups. Recommendations include: (a) future research in federal, provincial and territorial correctional systems, (b) identification of multiple risk factors that lead an individual to commit crime, (c) crime prevention strategies that help prevent criminal behavior for immigrants.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".