MétaCan
Menu
Back to cohort

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

2024· article· en· W4395463389 on OpenAlexaff
Jiale Wang, Hanyu Zhang

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.

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)0.006
metaresearch head score (Gemma)0.011
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0030.004
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCommunications in Humanities ResearchSame topicHealth Literacy and Information AccessibilityFrench-language works237,207