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Record W2081207426 · doi:10.3148/66.4.2005.215

<i>Gender and Nutrition Management</i> in Type 2 Diabetes

2005· article· en· W2081207426 on OpenAlexaffvenueabout
Mildred Wong, Enza Gucciardi, Louisa Li, Sherry L. Grace

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2005
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsDiabetes mellitusMeal preparationType 2 diabetesMedicineMealFamily medicineQualitative researchDiabetes managementGerontologyNursingPsychologyEndocrinologySociologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: The literature suggests that adherence to dietary recommendations may differ between women and men with type 2 diabetes due to family obligations and spousal support. METHODS: To assess division of household labour between spouses, retrospective chart review of 561 individuals who attended the Diabetes Education Centre at the Toronto Western Hospital was performed. Qualitative interviews were also performed with 12 married clients (six female and six male) and seven spouses of clients (three female, four male) to understand how the sharing of household labour influences adherence to nutrition guidelines in type 2 diabetes. RESULTS: Results indicate a significant gender difference in responsibility for meal preparation (chi2(3)=140.64, p<.001) and grocery shopping (chi2(3)=88.24, p<0.001), with women more often engaging in these household activities than men. Male clients are more likely to be actively supported by their wives in the form of meal preparation and verbal encouragement, while female clients are only passively supported by their husbands. CONCLUSIONS: The results suggest that diabetes educators should recognize gender differences in household labour and support when counselling their clients to ensure that both men and women have the help they need to successfully manage their diabetes.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.185

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.072
GPT teacher head0.385
Teacher spread0.313 · 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 designNot applicable
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

Citations42
Published2005
Admission routes3
Has abstractyes

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