Good intentions and dangerous assumptions: Research ethics committees and illicit drug use research
Bibliographic record
Abstract
Illicit drug users are frequently identified as a ‘vulnerable population’ requiring ‘special protection’ and ‘additional safeguards’ in research. However, without specific guidance on how to enact these special protections and safeguards, research ethics committee (REC) members sometimes fall back on untested assumptions about the ethics of illicit drug use research. In light of growing calls for ‘evidence-based research ethics’, this commentary examines three common assumptions amongst REC members about what constitutes ethical research with drug users, and whether such assumptions are borne out by a growing body of empirical data. The assumptions that form the focus of this commentary are as follows: (i) drug users do not have the capacity to provide informed consent to research; (ii) it is ethically problematic to provide financial incentives to drug users to participate in research; and (iii) asking drug users about their experiences ‘re-traumatizes’ and ‘re-victimizes’ them.
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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 | 0.261 | 0.370 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.123 |
| Scholarly communication | 0.025 | 0.028 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.057 | 0.049 |
| Insufficient payload (model declined to judge) | 0.002 | 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".