Symptom Experience of Older Adults With Type 2 Diabetes and Diabetes-Related Distress
Bibliographic record
Abstract
BACKGROUND: An older, more diverse population and longer life spans are major contributors to the anticipated tripling of Type 2 diabetes prevalence by 2050. Diabetes-related distress affects up to 40% of people diagnosed with Type 2 diabetes and may be a greater risk for older adults due to greater prevalence of comorbidities. OBJECTIVE: The objective of this phenomenological study was to describe how diabetes-related distress in older adults (≥65 years) with Type 2 diabetes might be uniquely experienced. METHODS: Participants were recruited using convenience sampling and snowball sampling. Interpretive phenomenology guided the research design and analysis. With interpretive interviews, we investigated the everyday health, symptoms, and life experiences of living with Type 2 diabetes and elevated diabetes distress. RESULTS: Among the older adults in this study, the most prevalent symptoms were fatigue, hypoglycemia, diarrhea, pain, loss of balance, and falling. These diabetes-related symptoms led to substantial loss of independence, decreased quality of life, and constrained social lives due to restricted activities. DISCUSSION: Diabetes-related distress presents with some unique symptoms and responses in older adults. Improving knowledge regarding the symptom experience of older adults with diabetes-related distress may allow healthcare providers to tailor treatment and thus improve outcomes for older adults struggling with diabetes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".