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Record W2089003687 · doi:10.1177/1524839911404225

Communicating Diabetes Best Practices to Clients

2011· article· en· W2089003687 on OpenAlexaffabout
Tanya R. Berry, Sven Anders, Rhonda C. Bell

Bibliographic record

VenueHealth Promotion Practice · 2011
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBest practiceDiabetes mellitusMedicineInternet privacyNursingPsychologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

PURPOSE: The majority of people with type 2 diabetes do not meet dietary and physical activity recommendations. It is not well understood how diabetes educators translate diet and physical guidelines for their clients and if diabetes educators have sufficient resources to promote healthy eating and physical activity. This research addressed these questions through exploratory qualitative interviews. METHOD: A total of 13 diabetes educators who work in Alberta, Canada, were interviewed. RESULTS: The reasons for lack of client uptake of lifestyle recommendations were complex and interwoven. The strongest theme to emerge was the clients' prior knowledge and skills affecting their ability to uptake knowledge. However, educators recognized that clients are affected by social, environmental, cultural, and personal factors. CONCLUSIONS: Health system and societal issues cause a cascade effect resulting in difficulties for both educators and clients. To achieve appropriate treatment of type 2 diabetes, changes need to occur at a health systems level.

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)0.010
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.088
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0040.002
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.002

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 head0.284
GPT teacher head0.463
Teacher spread0.179 · 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 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

Citations13
Published2011
Admission routes2
Has abstractyes

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