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Record W2078123421 · doi:10.1177/0898264310373502

Lifelong Educational Practices and Resources in Enabling Health Literacy Among Older Adults

2010· article· en· W2078123421 on OpenAlexaffabout
Andrew Wister, Leslie J. Malloy-Weir, Irving Rootman, Richard Desjardins

Bibliographic record

VenueJournal of Aging and Health · 2010
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsMcMaster UniversityUniversity of VictoriaSimon Fraser University
Fundersnot available
KeywordsLifelong learningReading (process)LiteracyHealth literacyPsychologyInformation literacyGerontologyMedical educationThe InternetPedagogyMedicineHealth careComputer sciencePolitical scienceWorld Wide Web

Abstract

fetched live from OpenAlex

OBJECTIVE: The goal of this study is to examine the role of lifelong educational and learning practices and resources in enabling health literacy. METHOD: A subsample of older adults (n = 2,979) derived from the 2003 seven country IALSS (Canadian survey) was used. An expanded Andersen-Newman model that included lifelong learning enabling factors was used to develop predictors of health literacy. RESULTS: The formal education, lifelong and lifewide learning enabling factors exhibited the most robust associations with health literacy. These included education level; self-study in the form of reading manuals, reference books and journals; computer/Internet use, use of the library; leisure reading of books; reading letters, notes and e-mails; and volunteerism. DISCUSSION: Findings are discussed in relation to the development and maintenance of health literacy over the life course. Programs and policies that encourage lifelong and lifewide educational resources and practices by older persons are needed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.043
GPT teacher head0.487
Teacher spread0.443 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations87
Published2010
Admission routes2
Has abstractyes

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