Public Opinions about Participating in Health Research
Bibliographic record
Abstract
OBJECTIVES: Privacy legislation has limited options for recruiting subjects to health studies. Policy changes are motivated by assumptions about public attitudes towards participation, yet surveys of attitudes have rarely been done. We investigated public willingness to participate in health research and how willingness was affected by various factors. METHODS: A survey of adults randomly selected from the telephone directory was conducted in British Columbia, Canada. Mailed self-administered questionnaires asked about willingness to participate in health research and the influence on willingness of the method of subject selection, the organization making the contact, and other factors. RESULTS: There were 1,477 respondents (58% of eligible); 85% were willing to participate in health research at least sometimes. The organization making the contact influenced comfort about participation: 10% of respondents felt uncomfortable if contacted by a university, 12% if by a hospital, 26% if by government, and 55% if by private research firms. Factors most positively influencing choice to participate were future health benefits to society (87%) and oneself (87%), and receiving a copy of the study results (81%). CONCLUSIONS: Participation in health research appears to be viewed favourably by members of the public, and participation may be highest when university or hospital-based researchers are able to contact subjects directly using information from government databases.
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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 | 0.167 | 0.412 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.044 | 0.037 |
| Insufficient payload (model declined to judge) | 0.009 | 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".