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Record W4309840114 · doi:10.1177/01640275221138968

“I Could Have Stood a Little More Education Rather than Just: ‘Hey, you’re Diabetic Man, Make the Best out of It’”: Revisioning Diabetes Self-Management Education for Older Adults

2022· article· en· W4309840114 on OpenAlexafffundabout
Madison Robertson, Geneviève C. Paré, Idevânia G. Costa, Beatriz Alvarado, Lenora Duhn, Pilar Camargo‐Plazas

Bibliographic record

VenueResearch on Aging · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsLakehead UniversityQueen's University
FundersCanadian Institutes of Health ResearchQueen's University
KeywordsPhotovoiceGerontologyDiabetes mellitusMedicineSelf-managementDiabetes managementQualitative researchPopulationPsychologyType 2 diabetesSociologyEnvironmental health

Abstract

fetched live from OpenAlex

Objectives: Providing diabetes self-management education (DSME) in an evidence-based format that is accessible and tailored to the population needs is crucial for individuals living with diabetes mellitus. Our qualitative study explores the experiences of older adults living with diabetes while residing in a rural setting. Methods: Adults aged 65 or older and residing in a rural area of Ontario completed a photovoice activity and semi-structured interviews to illustrate their experience of living with diabetes and accessing DSME. Results: Fourteen participants (11 males; mean age = 74 years) completed the photovoice activity and interview. Four main themes were identified pertaining to learning about diabetes education, the depth and breadth of learning, applying knowledge to daily life, and engaging older adults in DSME. Discussion: Diabetes self-management education should account for older adults’ preferences in learning about diabetes and self-management to promote access to evidence-based information, bolster knowledge and self-management efficacy, and improve disease control.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.059
GPT teacher head0.408
Teacher spread0.350 · 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 designOther design
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

Citations10
Published2022
Admission routes3
Has abstractyes

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