Ethics of health research with prisoners in Canada
Bibliographic record
Abstract
BACKGROUND: Despite the growing recognition for the need to improve the health of prisoners in Canada and the need for health research, there has been little discussion of the ethical issues with regards to health research with prisoners in Canada. The purpose of this paper is to encourage a national conversation about what it means to conduct ethically sound health research with prisoners given the current realities of the Canadian system. Lessons from the Canadian system could presumably apply in other jurisdictions. MAIN TEXT: Any discussion regarding research ethics with Canadian prisoners must begin by first taking into account the disproportionate number of Indigenous prisoners (e.g., 22-25% of prisoners are Indigenous, while representing approximately 3% of the general Canadian population) and the high proportion of prisoners suffering from mental illnesses (e.g., 45% of males and 69% of female inmates required mental health interventions while in custody). The main ethical challenges that researchers must navigate are (a) the power imbalances between them, the correctional services staff, and the prisoners, and the effects this has on obtaining voluntary consent to research; and (b), the various challenges associated to protecting the privacy and confidentiality of study participants who are prisoners. In order to solve these challenges, a first step would be to develop clear and transparent processes for ethical health research, which ought to be informed by multiple stakeholders, including prisoners, the correctional services staff, and researchers themselves. CONCLUSION: Stakeholder and community engagement ought to occur in Canada with regards to ethical health research with prisoners that should also include consultation with various parties, including prisoners, correctional services staff, and researchers. It is important that national and provincial research ethics organizations examine the sufficiency of existing research ethics guidance and, where there are gaps, to develop guidelines and help craft policy.
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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 | 0.066 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.025 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| 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; 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".