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Record W2144272309 · doi:10.1186/1478-4505-7-s1-s15

SUPPORT Tools for evidence-informed health Policymaking (STP) 15: Engaging the public in evidence-informed policymaking

2009· article· en· W2144272309 on OpenAlexaff
Andrew D Oxman, Simon Lewin, John N. Lavis, Atle Fretheim

Bibliographic record

VenueHealth Research Policy and Systems · 2009
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsMcMaster University
FundersDirektoratet for UtviklingssamarbeidAlliance for Health Policy and Systems ResearchEuropean Commission
KeywordsPublic relationsPublic healthHealth policyTransparency (behavior)AccountabilityHealth services researchPublic engagementHealth administrationPublic administrationPublic policyEvidence-based policyCivil societyPolitical scienceMedicineNursingPolitics

Abstract

fetched live from OpenAlex

This article is part of a series written for people responsible for making decisions about health policies and programmes and for those who support these decision makers. In this article, we address strategies to inform and engage the public in policy development and implementation. The importance of engaging the public (both patients and citizens) at all levels of health systems is widely recognised. They are the ultimate recipients of the desirable and undesirable impacts of public policies, and many governments and organisations have acknowledged the value of engaging them in evidence-informed policy development. The potential benefits of doing this include the establishment of policies that include their ideas and address their concerns, the improved implementation of policies, improved health services, and better health. Public engagement can also be viewed as a goal in itself by encouraging participative democracy, public accountability and transparency. We suggest three questions that can be considered with regard to public participation strategies. These are: 1. What strategies can be used when working with the mass media to inform the public about policy development and implementation? 2. What strategies can be used when working with civil society groups to inform and engage them in policy development and implementation? 3. What methods can be used to involve consumers in policy development and implementation?

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

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 armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.046
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.720
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0460.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0080.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.003
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 head0.853
GPT teacher head0.660
Teacher spread0.193 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
Domainnot available
GenreEmpirical · Methods

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

Citations81
Published2009
Admission routes1
Has abstractyes

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