Doing Research with Vulnerable Populations: The Case of Intravenous Drug Users
Bibliographic record
Abstract
This review article considers ethical concerns when doing research on potentially vulnerable people who inject drugs (PWID) in a Canadian context. The Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans broadly addresses many of the traditional ethical principles of research on vulnerable persons, but does so at the cost of clarity and precision. Vulnerability is contextual rather than absolute. When doing research with vulnerable persons, informed consent should be obtained from an independent person, and comprehension should be checked using questioning. Participants can be vulnerable due to many factors, including addiction, chronic disease, socioeconomic and racial status, and lack of education. The ability of PWID to give informed consent can be compromised by undue influence or intoxication, but existing research shows that neither the mode nor the magnitude of compensation has a significant effect on new rates of drug use. Compensation can also help dispel the therapeutic misconception. Intoxication rather than undue influence is the main concern when obtaining informed consent from PWID. The stigmatization of PWID as incapable of consent should be avoided. Paternalistic exclusion from research can harm PWID and exacerbate their vulnerability by reducing our knowledge of and ability to specifically treat them. As such, we must collect better data about the effects of research ethics policies. Studies to this effect should focus on experiences, perspectives and needs of potentially vulnerable research participants. Research ethics boards in Canada should adopt an evidence-based approach when applying discretionary power to proposals for clinical research.
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 | 0.079 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.036 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.015 | 0.009 |
| 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".