Monitoring systems and national surveys on prison health in France and abroad
Bibliographic record
Abstract
BACKGROUND: The implementation of a national monitoring system of prisoners' health is under consideration in France. As information available on this topic is quite scarce, particularly in Europe, a study was performed to identify and describe various prison health monitoring approaches implemented worldwide. METHODS: Data were collected for 15 countries in Oceania, North America and western and northern Europe via official state websites, bibliographical searches and interviews with international prison health representatives. RESULTS: The means and methods implemented to monitor prisoners' health in the studied countries are heterogeneous. Although all countries systematically record mortality data, only four have a monitoring system that covers a wide array of health data: Canada and Belgium routinely collect health data using a systematic, standardized and computerized approach, while the USA and Australia have developed regular repeated nationwide surveys. Some countries have set up monitoring systems restricted to specific health problems, such as infectious diseases (e.g. the UK, Switzerland and Canada) and mental health (e.g. New Zealand and the Netherlands). In other countries, including France, prisoners' health monitoring systems are limited to occasional epidemiological studies covering specific topics, for example, psychiatric disorders, addiction or infectious diseases. However, their one-off nature prevents regular assessment of health prevalence and trends. CONCLUSIONS: This study highlights the diversity of approaches and methods developed to monitor prison health in high-income countries. Analysis of these different situations provides an insight into the feasibility of and requirements for the development of an efficient prison health surveillance system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".