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Record W2030267987 · doi:10.1177/1010539512440592

Relationship Between Duration of Type 2 Diabetes and Self-Reported Participation in Diabetes Education in Korea

2012· article· en· W2030267987 on OpenAlexafffund
Jongnam Hwang, Jeffrey Johnson

Bibliographic record

VenueAsia Pacific Journal of Public Health · 2012
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsMedicineDiabetes mellitusOdds ratioConfidence intervalLogistic regressionSocioeconomic statusType 2 diabetesNational Health and Nutrition Examination SurveyDemographyGerontologyInternal medicineEnvironmental healthPopulationEndocrinology

Abstract

fetched live from OpenAlex

The increasing prevalence of diabetes is a pressing issue in Korea. The aim of this study was to determine the relationship between duration of diabetes and self-reported participation in diabetes education among diabetic patients in Korea. This study used data from the Korean National Health and Nutrition Examination Survey in 2005. A total of 1405 respondents older than 19 years and having diabetes were included in the analyses. The relationship between these variables was assessed using logistic regression after adjusting for age, sex, and socioeconomic status. The authors observed that duration of diabetes was associated with having never attended diabetes education programs (odds ratio = 0.95; 95% confidence interval = 0.93-0.96; P < .001), with the greatest risk of not attending seen in recently diagnosed patients. In addition, having lower educational attainment and living in non-Metro Seoul regions were independent factors for never attending diabetes education programs among diabetic patients in Korea. This finding suggests the need for developing effective education programs to encourage diabetic patients, particularly recently diagnosed patients, to participate. Such programs could help deliver appropriate information for diabetes management to all diabetic patients in Korea.

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 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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.026
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.076
GPT teacher head0.364
Teacher spread0.289 · 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 teacher head, 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

Citations5
Published2012
Admission routes2
Has abstractyes

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