Using a Delphi process to define priorities for prison health research in Canada
Bibliographic record
Abstract
OBJECTIVES: A large number of Canadians spend time in correctional facilities each year, and they are likely to have poor health compared to the general population. Relatively little health research has been conducted in Canada with a focus on people who experience detention or incarceration. We aimed to conduct a Delphi process with key stakeholders to define priorities for research in prison health in Canada for the next 10 years. SETTING: We conducted a Delphi process using an online survey with two rounds in 2014 and 2015. PARTICIPANTS: We invited key stakeholders in prison health research in Canada to participate, which we defined as persons who had published research on prison health in Canada since 1994 and persons in the investigators' professional networks. We invited 143 persons to participate in the first round and 59 participated. We invited 137 persons to participate in the second round and 67 participated. PRIMARY AND SECONDARY OUTCOME MEASURES: Participants suggested topics in the first round, and these topics were collated by investigators. We measured the level of agreement among participants that each collated topic was a priority for prison health research in Canada for the next 10 years, and defined priorities based on the level of agreement. RESULTS: In the first round, participants suggested 71 topics. In the second round, consensus was achieved that a large number of suggested topics were research priorities. Top priorities were diversion and alternatives to incarceration, social and community re-integration, creating healthy environments in prisons, healthcare in custody, continuity of healthcare, substance use disorders and the health of Aboriginal persons in custody. CONCLUSIONS: Generated in an inclusive and systematic process, these findings should inform future research efforts to improve the health and healthcare of people who experience detention and incarceration in Canada.
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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 | 0.296 | 0.193 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.023 | 0.013 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".