A Process Model of Formative Work to Strengthen a Prison Health Surveillance System
Bibliographic record
Abstract
Worldwide, there is a lack of systematically collected health data on people who are incarcerated. Our objective in this paper was to describe a process model of formative work for a project to strengthen health surveillance for people incarcerated under a Canadian prison authority. We have developed project structures and processes, and we are evaluating project partnerships. To inform prison health surveillance foci, we are conducting a review of literature on best practices, a qualitative study to understand stakeholders' needs and priorities, and mapping work to understand available prison health-related data. Developing and implementing prison health surveillance is gradual and developmental, necessitating time to build relationships and obtain approvals. The needs and interests of knowledge users should be prioritized, but there may be challenges to achieving a coherent vision due to feasibility and differing needs and objectives of various stakeholders. Developing collaborative relationships could help bridge this gap.
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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 | 0.071 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".