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Record W2625615514 · doi:10.1177/1203475417715208

Using Visual Aids to Enhance Physician-Patient Discussions and Increase Health Literacy

2017· article· en· W2625615514 on OpenAlexaff
M.E. Pratt, Gordon E. Searles

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of AlbertaMemorial University of Newfoundland
Fundersnot available
KeywordsHealth literacyPictogramMedicineComprehensionNumeracyLiteracyMedical educationVisual literacyFamily medicineHealth carePsychologyComputer scienceLinguisticsMathematics educationPedagogy

Abstract

fetched live from OpenAlex

Health literacy refers to the comprehension required to make well-informed decisions regarding one's health. It is a critical component in helping patients to understand how to take their medications appropriately. However, many patients do not possess the comprehension necessary for medication adherence. The result of poor literacy is a higher incidence of misunderstanding medication instructions. Visual aids have the ability to transcend language and numeracy barriers and can therefore improve the effectiveness of communication and broaden target audiences. To enhance communication that is language independent, a template was created to provide instructions for proper use and explanation of risks for adverse events. This template is designed to fit on a single double-sided page. This template can be adapted for use in explaining any medication using universal pictograms available from online resources. This would enable any practitioner to design information sheets for their unique use.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.068
GPT teacher head0.501
Teacher spread0.433 · 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 designOther design
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

Citations48
Published2017
Admission routes1
Has abstractyes

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