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Record W2314277051 · doi:10.1093/heapro/daw003

Health literacy and chronic disease management: drawing from expert knowledge to set an agenda

2016· article· en· W2314277051 on OpenAlex
Iraj Poureslami, Laura Nimmon, Irving Rootman, Mark J. Fitzgerald

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHealth Promotion International · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
Fundersnot available
KeywordsHealth literacyHealth promotionLiteracyPsychological interventionVariety (cybernetics)MedicinePublic relationsHealth policyMedical educationHealth carePolitical scienceNursingPsychologyPublic healthPedagogyComputer science

Abstract

fetched live from OpenAlex

Understanding the nature and impact of health literacy is a priority in health promotion and chronic disease prevention and treatment. Health literacy comprises the application of a broad set of skills to access, comprehend, evaluate, communicate and act on health information for improved health and well-being. A complex concept, it involves multiple participants and is enacted across a wide variety of contexts. Health literacy's complexity has given rise to challenges achieving a standard definition and developing means to measure all its dimensions. In May 2013, a group of health literacy experts, clinicians and policymakers convened at an Expert Roundtable to review the current state of health literacy research and practice, and make recommendations about refining its definition, expanding its measurement and integrating best practices into chronic disease management. The four-day knowledge exchange concluded that the successful integration of health literacy into policy and practice depends on the development of a more substantial evidence base. A review of the successes and gaps in health literacy research, education and interventions culminated in the identification of key priorities to further the health literacy agenda. The workshop was funded by the UBC Peter Wall Institute for Advanced Studies, Vancouver.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.878
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.095
GPT teacher head0.512
Teacher spread0.416 · 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