Political Ideology, Confidence in Science, and Participation in Alzheimer Disease Research Studies
Bibliographic record
Abstract
BACKGROUND: Americans' confidence in science varies based on their political ideology. This ideological divide has potentially important effects on citizens' engagement with and participation in clinical studies of Alzheimer disease (AD). METHODS: A probability sample of 1583 Americans was surveyed about their willingness to participate in longitudinal AD research and about their political attitudes. These survey results were compared with a survey of 382 participants in a longitudinal AD study at the Knight Alzheimer Disease Research Center. RESULTS: Among Americans, more conservative ideology decreases willingness to participate in a hypothetical longitudinal cohort study of AD both directly and through its negative effect on confidence in science. The Knight Alzheimer Disease Research Center study participants expressed more liberal ideology and greater confidence in science than Americans in general. Of the survey respondents opposed to participation, over a quarter changed to neutral or positive if the study returned their research results to them. CONCLUSIONS AND RELEVANCE: Clinical studies of AD are likely biased toward participants who are more liberal and have higher confidence in science than the general population. This recruitment bias may be reduced by lowering the trust demanded of participants through measures such as returning research results to participants.
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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".