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Record W2786175694 · doi:10.3233/978-1-61499-830-3-178

Are Health Literacy and eHealth Literacy the Same or Different?

2017· article· en· W2786175694 on OpenAlex
Helen Monkman, André Kushniruk, Jeff Barnett, Elizabeth M. Borycki, Leigh Greiner, Debra Sheets

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.

Bibliographic record

VenueStudies in health technology and informatics · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordseHealthHealth literacyLiteracyComputer sciencePsychologyPolitical scienceHealth carePedagogy

Abstract

fetched live from OpenAlex

Many researchers assume that there is a relationship between health literacy and eHealth literacy, yet it is not clear whether the literature supports this assumption. The purpose of this study was to determine if there was a relationship between health and eHealth literacy. To this end, participants' (n = 36) scores on the Newest Vital Sign (NVS, a health literacy measure) were correlated with the eHealth Literacy Scale (eHEALS, an eHealth literacy measure). This analysis revealed no relationship (r = -.041, p = .81) between the two variables. This finding suggests that eHealth Literacy and health literacy are dissimilar. Several possible explanations of the pattern of results are proposed. Currently, it does not seem prudent to use the eHEALS as the sole measure of eHealth literacy, but rather researchers should continue to complement it with a validated health literacy screening tool.

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)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience 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 score0.280
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0090.001
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.002
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 head0.123
GPT teacher head0.528
Teacher spread0.404 · 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