Weighing Open Science Against Research Participation Burden in Informed Consent: A Randomized Pilot Study
Bibliographic record
Abstract
Deciding to participate in clinical research requires patients to evaluate various trial characteristics. This survey study measured the influence of trial characteristics (visit volume and open data sharing) on willingness to participate in patients with Parkinson's disease. Patients were randomized to either evaluate two trials (joint condition) or a single trial (separate condition). For patients who evaluated both trials, willingness to participate was greater in the restricted science protocol with fewer visits (7.60 vs. 7.12; mean difference, -0.48; 95% CI, -0.83 to -0.14). Patients who evaluated both trials were less willing to participate in the study involving more visits than patients who only evaluated the study involving more visits (7.12 vs. 8.14; mean difference, 1.02; 95% CI, 0.30 to 1.74). Patients have difficulty evaluating information about open science and visit burden when information is presented separately. Although views toward open science were favorable, preferences for fewer visits dominated decisions.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchOpen science Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
| gpt | MetaresearchOpen science Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
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 | 0.068 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".