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Record W1646294165

An Investigation Of Health Literacy, Acculturation, Diabetes Knowledge, And Social Supports Among Latinos With Diabetes In Southern Ontario

2015· article· en· W1646294165 on OpenAlexaboutno aff
Ivonne Aguilar

Bibliographic record

VenueScholarship@Western (Western University) · 2015
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsnot available
Fundersnot available
KeywordsAcculturationLiteracyDiabetes mellitusHealth literacyGerontologyMedicineEthnic groupPsychologySociologyPolitical scienceAnthropologyHealth carePedagogy
DOInot available

Abstract

fetched live from OpenAlex

The prevalence of diabetes is high among Latino people. This study investigated the relationships between health literacy, acculturation, social support, and diabetes-related knowledge among Latino adults with Type 2 diabetes and informal caregivers with a family history of Type 2 diabetes. A non-experimental, cross-sectional design was used to examine these relationships among 73 adult Latino participants living in Southern Ontario. Based on Nutbeam’s conceptualization of health literacy, associations existed between health literacy, acculturation and knowledge of diabetes among Latino participants, yet the concept of social support showed no direct relationship to health literacy. This research has explored the factors that influence health literacy, and how limited health literacy may have detrimental effects on health outcomes for Latino people with diabetes and/or their family caregivers. Thus, this research is crucial for the planning and implementation of diabetes programs aimed at Latino populations to improve their management of chronic disease.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.067
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.070
GPT teacher head0.314
Teacher spread0.243 · 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
Published2015
Admission routes1
Has abstractyes

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