Differences in Secure Messaging, Self-management, and Glycemic Control Between Rural and Urban Patients: Secondary Data Analysis
Bibliographic record
Abstract
BACKGROUND: Rural patients with diabetes have difficulty accessing care and are at higher risk for poor diabetes management. Sustained use of patient portal features such as secure messaging (SM) can provide accessible support for diabetes self-management. OBJECTIVE: This study explored whether rural patients' self-management and glycemic control was associated with the use of SM. METHODS: This secondary, cross-sectional, mixed methods analysis of 448 veterans with diabetes used stratified random sampling to recruit a diverse sample from the United States (rural vs urban and good vs poor glycemic control). Administrative, clinical, survey, and interview data were used to determine patients' rurality, use of SM, diabetes self-management behaviors, and glycemic control. Moderated mediation analyses assessed these relationships. RESULTS: The sample was 51% (n=229) rural and 49% (n=219) urban. Mean participant age was 66.4 years (SD 7.7 years). More frequent SM use was associated with better diabetes self-management (P=.007), which was associated with better glycemic control (P<.001). Among rural patients, SM use was indirectly associated with better glycemic control through improved diabetes self-management (95% CI 0.004-0.927). These effects were not observed among urban veterans with diabetes (95% CI -1.039 to 0.056). Rural patients were significantly more likely than urban patients to have diabetes-related content in their secure messages (P=.01). CONCLUSIONS: More frequent SM use is associated with engaging in diabetes self-management, which, in turn, is associated with better diabetes control. Among rural patients with diabetes, SM use is indirectly associated with better diabetes control. Frequent patient-team communication through SM about diabetes-related content may help rural patients with diabetes self-management, resulting in better glycemic 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".