“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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".