Understanding An Informed Public’s Views On The Role Of Evidence In Making Health Care Decisions
Bibliographic record
Abstract
Policy makers and practitioners increasingly believe that medical evidence plays a critical role in improving care and health outcomes and lowering costs. However, public understanding of the role of evidence-based care may be different. Public deliberation is a process that convenes diverse citizens and has them learn about and consider ethical or values-based dilemmas and weigh alternative views. The Community Forum Deliberative Methods Demonstration project, sponsored by the Agency for Healthcare Research and Quality, obtained informed public views on the role of evidence in health care decisions through seventy-six deliberative groups involving 907 people overall, in the period August-November 2012. Although participants perceived evidence as being essential to high-quality care, they also believed that personal choice or clinical judgment could trump evidence. They viewed doctors as central figures in discussing evidence with patients and key arbiters of whether to follow evidence in individual cases. They found evidence of harm to individuals or the community to be more compelling than evidence of effectiveness. These findings indicate that increased public understanding of evidence can play an important role in advancing evidence-based care by helping create policies that better reflect the needs and values of the public.
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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 | 0.235 | 0.261 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.010 | 0.052 |
| Scholarly communication | 0.033 | 0.032 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.021 | 0.030 |
| Insufficient payload (model declined to judge) | 0.003 | 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".