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Record W2775639239 · doi:10.12927/hcpol.2017.25323

What’s Involved with Wanting to Be Involved? Comparing Expectations for Public Engagement in Health Policy across Research and Care Contexts

2017· article· en· W2775639239 on OpenAlexaffvenue
Carolyn J. Barg, Fiona A. Miller, Robin Z. Hayeems, Yvonne Bombard, Céline Cressman, Michael Painter‐Main

Bibliographic record

VenueHealthcare policy · 2017
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsInstitute for Work & HealthSt. Michael's HospitalUniversity of TorontoSickKids FoundationHospital for Sick ChildrenInstitute of Health Services and Policy Research
Fundersnot available
KeywordsPublic engagementPsychologyPublic healthPublic relationsPolitical scienceMedicineNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: We explored public preferences for involvement in health policy decisions, across the contexts of medical research and healthcare. APPROACH: We e-surveyed a sample of Canadians, categorizing respondents by preferences for decision control: (1) more authority; (2) more input; (3) status quo. Two generalized ordered logistic regressions assessed influences on preferences. RESULTS: The participation rate was 94%; 1,102 completed responses met quality criteria. The dominant preference was for more input (average = 52.0%), followed by status quo (average = 24.9%) and more authority (average = 21.1%), though preferences for more control were higher in healthcare (57.2%) than medical research (46.8%). Preferences for greater control were associated with constructs related to reduced trust in healthcare systems. CONCLUSION: The public expects health policy to account for public views, but not base decisions primarily on these views. More involvement was expected in healthcare than medical research policy. As opportunities for public involvement in health research grow, we anticipate increased desired involvement.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0130.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.671
GPT teacher head0.600
Teacher spread0.071 · 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

Citations10
Published2017
Admission routes2
Has abstractyes

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