Paying research subjects: participants' perspectives
Bibliographic record
Abstract
OBJECTIVE: To explore the opinions of unpaid healthy volunteers on the payment of research subjects. DESIGN: Prospective cohort. SETTING: Southern Alberta, Canada. PARTICIPANTS: Medically eligible persons responding to recruiting advertisements for a randomised vaccine trial were invited to take part in a study of informed consent at the point at which they formally consented or refused trial participation. Of 72 invited, 67 (62 trial consenters, 5 trial refusers) returned questionnaires at baseline and 54 at follow-up. OUTCOME MEASURES: Proportions of persons who agreed or disagreed with three close-ended statements on the payment of research subjects; themes and categories identified by content analysis of responses to an open-ended question. RESULTS: A minority (43.3%) agreed with paying either patient or healthy volunteer participants. Opinions did not change over time. Participants' comments addressed: benefits and drawbacks to research participation; benefits and drawbacks to paying research participants; conditions under which payment of research subjects would be acceptable, and the nature of acceptable recognition. Acceptable conditions were to improve problematic recruitment, to reimburse costs, and to recognise participants, particularly for their time investment. Both non-monetary and monetary recognition of volunteers were thought to be appropriate. CONCLUSIONS: Most unpaid volunteers disagreed with paying research participants. The themes arising from their comments are similar to those that have been raised by ethicists and suggest that recognising the time and effort of participants should receive greater emphasis than presently occurs.
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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 | 0.064 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| 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".