Do Canadian Researchers and the Lay Public Prioritize Biomedical Research Outcomes Equally? A Choice Experiment
Bibliographic record
Abstract
PURPOSE: To quantify and compare the preferences of researchers and laypeople in Canada regarding the outcomes of basic biomedical research. METHOD: In autumn 2010, the authors conducted a cross-sectional, national survey of basic biomedical researchers funded by Canada's national health research agency and a representative sample of Canadian citizens to assess preferences for research outcomes across five attributes using a discrete choice experiment. Attributes included advancing scientific knowledge (assessed by published papers); building research capacity (assessed by trainees); informing decisions in the health products industry (assessed by patents); targeting economic, health, or scientific priorities; and cost. The authors reduced a fractional factorial design (18 pairwise choices plus an opt-out option) to three blocks of six. They also computed part worth utilities, differences in predicted probabilities, and willingness-to-pay values using nested logit models. RESULTS: Of 3,260 potential researchers, 1,749 (53.65% response rate) completed the questionnaire, along with 1,002 citizens. Researchers and citizens prioritized high-quality scientific outcomes (papers, trainees) over other attributes. Both groups disvalued research targeted at economic priorities relative to health priorities. Researchers granted a premium to proposals targeting scientific priorities. CONCLUSIONS: Citizens and researchers fundamentally prioritized the same outcomes for basic biomedical research. Notably, they prioritized traditional scientific outcomes and disvalued the pursuit of economic returns. These findings have implications for how academic medicine assigns incentives and value to basic health research and how biomedical researchers and the public may jointly contribute to setting the future research agenda.
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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 | 0.026 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".