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Record W2097690199 · doi:10.12927/hcpol.2015.24034

Supporting Evidence-Informed Health Policy Making: The Development and Contents of an Online Repository of Policy-Relevant Documents Addressing Healthcare Renewal in Canada

2014· article· en· W2097690199 on OpenAlexaffvenueabout
Karolina Kowalewski, John N. Lavis, Michael G. Wilson, Nancy Carter

Bibliographic record

VenueHealthcare policy · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHealth careProcess (computing)Public relationsPolicy developmentBusinessHealthcare policyResource (disambiguation)Political sciencePeer reviewHealth policyKnowledge managementComputer sciencePublic administrationHealth care reform

Abstract

fetched live from OpenAlex

OBJECTIVES: (1) To develop an online repository of policy-relevant documents, other than and complementary to those from the peer-reviewed scientific literature, addressing healthcare renewal in Canada; and (2) to describe the distribution of document contents. METHODS: An iterative scoping review approach was undertaken. Documents were identified through website hand-searches and referrals from 19 Canadian health organizations. Descriptive frequencies were calculated, such as for document type. FINDINGS: In July 2014, 1," documents were in the Evidence-Informed Healthcare Renewal Portal. The top three types of documents were situation analyses (n = 390, 38%), health and health system data (n = 191, 18%) and jurisdictional reviews (n = 115, 11%). The top three national priority areas addressed were health human resources (n = 778, 75%), quality as a performance indicator (n = 502, 49%) and information technology (n = 385, 37%). CONCLUSION: The process of developing a systematic method for identifying these documents has yielded a new resource to support evidence-informed health policy making and has identified a large volume of policy-relevant documents addressing healthcare renewal priority areas in Canada.

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)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0000.000
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 head0.501
GPT teacher head0.640
Teacher spread0.139 · 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 designObservational
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

Citations4
Published2014
Admission routes3
Has abstractyes

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