The big challenge out here is getting stuff: How the social determinants of health affect diabetes self-management education for seniors
Bibliographic record
Abstract
In Canada, diabetes self-management education (DSME) programs are offered to enable individuals with diabetes to successfully implement and sustain lifestyle changes, with the goal of reducing risk of complications and morbidity. Researchers have demonstrated how older adults with diabetes often fail to achieve or maintain diabetes self-management (DSM) competencies, increasing complication risk. Further, little is known about the influence of the social determinants of health (SDH) on DSME, potentially producing additional inequalities for these adults; the WHO defines SDH as non-medical factors (e.g., education; food insecurity) that impact health outcomes. The study goal was to better understand how the SDH affect DSME for older adults living with diabetes. Methods: In our qualitative study we used participatory, art-based, and hermeneutic phenomenology research methodologies. Data collection included photovoice and semi-structured phone interviews. Fourteen older adults with diabetes participated (11 men, 3 women; aged 65 years or older). A SDH framework (Loppie-Reading and Wien) guided the thematic analysis. Results: The findings illuminate how participants live with the effects and pressures of the SDH. Proximal determinants of health revealed in participants’ stories included health behaviours (diabetes self-management practices), physical environments (rurality), socioeconomic status (income), and food insecurity (accessing healthy food). Intermediate determinants comprised health-care systems (accessing DSME in their community) and community resources and capacities (limited infrastructure due to rurality). Distal determinants involved the pandemic (isolation due to mobilization restrictions). Conclusion: Our study demonstrated how the SDH affect DSME and DSM for older adults. Participants were continuously rearranging their diabetes needs to accommodate other life priorities. Additionally, rural living is described as a barrier to DSM, as accessing diabetes education, food, medications, and gas is at a distance—a particular inconvenience during wintertime. Our findings will guide future design, planning and implementation of DSME programs for older adults in this rural setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".