Interest-holder priorities for health surveillance of people incarcerated in Canada: A qualitative study
Bibliographic record
Abstract
OBJECTIVES: As a core function of public health, health surveillance for people who are incarcerated could address gaps in knowledge regarding their health status. The views of people who could use these data and who are included in health surveillance data should inform health surveillance. In this study, we aimed to identify the priorities of interest-holders for health surveillance of people incarcerated in Canadian correctional facilities. STUDY DESIGN: We conducted an overall qualitative descriptive study with an embedded reflexive thematic analysis and a qualitative content analysis. METHODS: We conducted virtual or phone-based focus groups and interviews with people across Canada, including people with lived experience of incarceration, community-based advocates and researchers, and current correctional health care staff and leadership. RESULTS: Overall, 61 participants took part. We describe two types of interest-holder priorities: health conditions and issues, which we identified using content analysis, and health care characteristics and components, which we constructed through thematic analysis. The top priorities for health conditions and issues to track and monitor were mental health issues, substance use disorders and harm reduction, chronic diseases, and nutrition, diet, and healthy food. The health care themes that were priorities for health surveillance were access, wait times, health care equivalence, preventive care, and medication administration. CONCLUSIONS: This study begins to fill the gap in population level health data for people who are incarcerated. Findings should have relevance for correctional authorities both within Canada and in other jurisdictions.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".