Community‐based models of health care for women, trans and nonbinary people released from prisons: An international scoping review with implications for Canada
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To illustrate the scope of different types of transitional, community-based health interventions for formerly incarcerated women, trans and nonbinary people, the eligibility criteria for these interventions, and associated health outcomes. BACKGROUND: Meeting the health needs of formerly incarcerated people in community, rather than through the criminal justice system, may prevent further experiences of criminalization. Research is needed to understand what community-based health interventions have been implemented internationally to inform the design of an intervention in Canada. DESIGN: Scoping review using the Joanna Briggs Institute scoping review methodology. METHODS: In consultation with a medical research librarian, key databases and journals were searched for English language articles, from any country, with no specified date range. Three authors independently screened titles and abstracts to identify articles for full-text review. The study adheres to PRISMA-EQUATOR guidelines. RESULTS: Thirty-six studies met the present criteria and were reviewed in full text. Method, setting, participants, sample, relevant outcomes and relevant findings were extracted from each study for synthesis. Included studies had varied methods and were published from 1999 to 2020. Thirty-one studies were based in the United States, one in Puerto Rico and two each in Canada and the United Kingdom. The most common health issue focus was human immunodeficiency virus and/or hepatitis c virus. The most common outcome was uptake of offered services, such as a transitional clinic. CONCLUSIONS: Gaps in the research pertain to a lack of attention to clinical outcomes and patient experience, and a lack of consideration of sexual and reproductive health concerns. Women were the minority population in all studies that included both men and women; transgender participants were mentioned in only four of thirty-six studies. The specific needs of women, trans and nonbinary people must be taken into consideration. RELEVANCE TO CLINICAL PRACTICE: Nurses must be conscious of the elevated health risks associated with exposure to correctional institutions and the risks associated with the period of transition. PATIENT OR PUBLIC CONTRIBUTION: A member of the research team brings lived experience expertise with respect to the criminal justice system.
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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 | 0.039 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.017 | 0.024 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".