MétaCan
Menu
Back to cohort
Record W2318410221 · doi:10.1377/hlthaff.2015.1112

Understanding An Informed Public’s Views On The Role Of Evidence In Making Health Care Decisions

2016· article· en· W2318410221 on OpenAlexaff
Kristin L. Carman, Maureen Maurer, Rikki Mangrum, Manshu Yang, Marjorie Ginsburg, Shoshanna Sofaer, Marthe R. Gold, Ela Pathak‐Sen, Dierdre Gilmore, Jennifer Richmond, Joanna E. Siegel

Bibliographic record

VenueHealth Affairs · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsRichmond Hospital
FundersU.S. Public Health Service
KeywordsDeliberationPublic relationsHarmAgency (philosophy)Health careEvidence-based practicePublic healthEvidence-based medicineQuality (philosophy)Public involvementPsychologyPolitical scienceMedicineNursingMEDLINESocial psychologyAlternative medicineSociologyLawPolitics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.913
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.935
GPT teacher head0.693
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueHealth AffairsSame topicHealth Policy Implementation ScienceFrench-language works237,207