Slipping through: mobility’s influence on infectious disease risks for justice-involved women in Canada
Bibliographic record
Abstract
BACKGROUND: The relationship between incarceration and women's vulnerability to sexually transmitted and blood-borne infections (STBBI) is understudied in Canada, despite numerous studies showing that justice-involved women experience very high rates of infection. Justice-involved women in Canada are highly mobile, as a result of high rates of incarceration and extremely short sentences. From a public health perspective, it is productive to understand how the mobility of justice-involved women shapes their vulnerability to STBBI. RESULTS: This narrative review demonstrates that mobility between incarceration facilities and communities drives sexually transmitted and blood-borne disease risk for justice-involved women in Canada. Associations and interactions between epidemics of gender-based and intimate partner violence, substance use, and STBBIs shape the experiences of justice-involved women in Canada. In correctional facilities, the pre-existing vulnerability of justice-involved women is compounded by a lack of comprehensive STBBI care and limited harm reduction services. On release, unstable housing, disruptions to social support networks, interruptions in medical care, and relapse to or continuation of substance use, significantly increase individual disease risk and the likelihood of community transmission. High rates of incarceration for short periods perpetuate this cycle and complicate the delivery of healthcare. CONCLUSIONS: The review provides evidence of the need for stronger gender-transformative public health planning and responses for incarcerated women, in both federal and provincial corrections settings in Canada. A supportive, evidence-based approach to STBBI identification and treatment for incarcerated women - one that that removes stigma, maintains privacy and improves access, combined with structural policies to prevent incarceration - could decrease STBBI incidence and interrupt the cycle of incarceration and poor health outcomes. A coordinated and accountable program of reintegration that facilitates continuity of public health interventions for STBBI, as well as safe housing, harm reduction and other supports, can improve outcomes as well. Lastly, metrics to measure performance of STBBI management during incarceration and upon release would help to identify gaps and improve outcomes for justice-involved women in the Canadian context.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".