Canadian research ethics board members’ attitudes toward benefits from clinical trials
Bibliographic record
Abstract
BACKGROUND: While ethicists have for many years called for human subject trial participants and, in some cases, local community members to benefit from participation in pharmaceutical and other intervention-based therapies, little is known about how these discussions are impacting the practice of research ethics boards (REBs) that grant ethical approval to many of these studies. METHODS: Telephone interviews were conducted with 23 REB members from across Canada, a major funder country for human subject research internationally. All interviews were digitally recorded and transcribed verbatim. After coding, the data was analyzed to identify central themes and topics. Themes were identified, application of the themes was confirmed, and these themes were then used to populate the findings of this manuscript. RESULTS: Our analysis of the interviews identified two primary themes when considering what benefits are owed to research participants and their communities. 1) Most study participants felt that given that these studies are led by persons in the role of researcher rather than health care provider, they had a limited obligation to provide benefits to study participants. 2) These REB members were all working in Canada, a high income country where most residents enjoy high levels of access to health care. As a result of this context, the study participants tended to focus on ethical concerns including obtaining informed consent and avoiding undue inducement to participate in research rather than ensuring that study participants directly benefit from successful trials. CONCLUSIONS: Research on REB members' attitudes toward what benefits are owed to study participants and community members is needed in other countries in order to determine how context affects these attitudes.
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 | 0.178 | 0.315 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.020 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".