Global Prison Health Care Governance and Health Equity: A Critical Lack of Evidence
Bibliographic record
Abstract
The large and growing population of people who experience incarceration makes prison health an essential component of public health and a critical setting for reducing health inequities. People who experience incarceration have a high burden of physical and mental health care needs and have poor health outcomes. Addressing these health disparities requires effective governance and accountability for prison health care services, including delivery of quality care in custody and effective integration with community health services.Despite the importance of prison health care governance, little is known about how prison health services are structured and funded or the methods and processes by which they are held accountable. A number of national and subnational jurisdictions have moved prison health care services under their ministry of health, in alignment with recommendations by the World Health Organization and the United Nations Office on Drugs and Crime. However, there is a critical lack of evidence on current governance models and an urgent need for evaluation and research, particularly in low- and middle-income countries.Here we discuss why understanding and implementing effective prison health governance models is a critical component of addressing health inequities at the global level.
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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 | 0.072 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".