Canadian Institutes of Health Research funding of prison health research: a descriptive study
Bibliographic record
Abstract
BACKGROUND: Health research provides a means to define health status and to identify ways to improve health. Our objective was to define the proportion of grants and funding from the Government of Canada's health research investment agency, the Canadian Institutes of Health Research (CIHR), that was awarded for prison health research, and to describe the characteristics of funded grants. METHODS: In this descriptive study, we defined prison health research as research on the health and health care of people in prisons and at the time of their release. We searched the CIHR Funding Decisions Database by subject and by investigator name for funded grants for prison health research in Canada in all competitions between 2010 and 2014. We calculated the proportion of grants and funding awarded for prison health research, and described the characteristics of funded grants. RESULTS: During the 5-year study period, 21 grants were awarded that included a focus on prison health research, for a total of $2 289 948. Six of these grants were operating grants and 6 supported graduate or fellowship training. In total, 0.13% of all grants and 0.05% of all funding was for prison health research. INTERPRETATION: A relatively small proportion of CIHR grants and funding were awarded for prison health research between 2010 and 2014. If prison health is a priority for Canada, strategic initiatives that include funding opportunities could be developed to support prison health research in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.019 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".