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Exploring Sociopsychological Determinants and Interventions for Enhancing Health Literacy: A Multifaceted Approach

2024· article· en· W4395463389 on OpenAlex

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

VenueCommunications in Humanities Research · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHealth literacyHealth promotionHealth equitySocial determinants of healthPsychological interventionPublic healthPublic relationsHealth educationHealth policyPsychologySocioeconomic statusHealth careLiteracyPolitical scienceEnvironmental healthMedicineNursingPedagogyPopulation

Abstract

fetched live from OpenAlex

Health literacy, the ability to obtain, understand, and utilize health information to make informed decisions, is essential for promoting public health and reducing health disparities. This paper examines the sociopsychological determinants of health literacy, focusing on individual factors, interpersonal dynamics, and societal contexts. Specifically, it explores the influence of cognitive abilities, health beliefs, socioeconomic status, social support, family and peer influences, healthcare systems, health policy, and cultural competence on health literacy levels. Additionally, the paper discusses interventions to enhance health literacy, including education and health promotion programs, community-based initiatives, and digital health technologies. By synthesizing insights from social psychology and social work, the paper underscores the importance of addressing multifaceted factors shaping health literacy and promoting equitable access to health information and services.

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)0.012
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.599
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
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 head0.853
GPT teacher head0.681
Teacher spread0.172 · 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