Time, Technology, Social Support, and Cardiovascular Health of Emerging Adults With Type 1 Diabetes
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Emerging adults with Type 1 diabetes (T1DM) face an increased risk of cardiovascular disease; however, there are both barriers and facilitators to achieving ideal cardiovascular health in this stage of their lives. OBJECTIVES: The aim of this study was to qualitatively explore the barriers and facilitators of achieving ideal levels of cardiovascular health in a sample of emerging adults with T1DM ages 18-26 years. METHODS: A sequential mixed-methods design was used to explore achievement of ideal cardiovascular health using the seven factors defined by the American Heart Association (smoking status, body mass index, physical activity, healthy diet, total cholesterol, blood pressure, and hemoglobin A1C [substituted for fasting blood glucose]). We assessed the frequency of achieving ideal levels of each cardiovascular health factor. Using Pender's health promotion model as a framework, qualitative interviews explored the barriers and facilitators of achieving ideal levels of each factor of cardiovascular health. RESULTS: The sample was mostly female. Their age range was 18-26 years, with a diabetes duration between 1 and 20 years. The three factors that had the lowest achievement were a healthy diet, physical activity at recommended levels, and hemoglobin A1C of <7%. Participants described lack of time as a barrier to eating healthy, being physically active, and maintaining in-range blood glucose levels. Facilitators included the use of technology in helping to achieve in-range blood glucose and social support from family, friends, and healthcare providers in maintaining several healthy habits. DISCUSSION: These qualitative data provide insight into how emerging adults attempt to manage their T1DM and cardiovascular health. Healthcare providers have an important role in supporting these patients in establishing ideal cardiovascular health at an early age.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it