Post-Release Health Insurance Utilization Among Ex-Prisoners: A Scoping Review
Bibliographic record
Abstract
The lack of health insurance coverage after prison release significantly reduces access to essential healthcare services, hindering the continuity of care during community reintegration. The evidence on studies of health insurance use following prison release is limited. This scoping review aimed to summarize research on health insurance utilization after release from prison. Literature searches were conducted across databases including ScienceDirect, PubMed, and Scopus. Relevant articles were selected through a two-stage screening process. Data were extracted from the included studies and presented in tabular and descriptive formats. The keywords used were "health insurance AND post-release" and "inmates OR prisoners." This scoping review showed that post-release health insurance utilization varied internationally. Coverage gaps, such as those in the United States, limit access to and continuity of care, whereas Canada and Australia provide more stable services. Barriers included administrative challenges, housing and employment instability, stigma, and poor coordination within the health system. The findings highlight the influence of national insurance frameworks and the need for policies supporting prerelease-release enrollment, coverage continuity, cross-sector collaboration, and adherence to the World Health Organization and Nelson Mandela Rules for equitable healthcare.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".