Veterans' Preferences for Remote Management of Chronic Conditions
Bibliographic record
Abstract
BACKGROUND: The Veterans Health Administration (VA) is investing considerable resources into providing remote management care to patients for disease prevention and management. Remote management includes online patient portals, e-mails between patients and providers, follow-up phone calls, and home health devices to monitor health status. However, little is known about patients' attitudes and preferences for this type of care. This qualitative study was conducted to better understand patient preferences for receiving remote care. METHODS: Ten focus groups were held comprising 77 patients with hypertension or tobacco use history at two VA medical centers. Discussion questions focused on experience with current VA remote management efforts and preferences for receiving additional care between outpatient visits. RESULTS: Most participants were receptive to remote management for referrals, appointment reminders, resource information, and motivational and emotional support between visits, but described challenges with some technological tools. Participants reported that remote management should be personalized and tailored to individual needs. They expressed preferences for frequency, scope, continuity of provider, and mode of communication between visits. Most participants were open to nonclinicians contacting them as long as they had direct connection to their medical team. Some participants expressed a preference for a licensed medical professional. All groups raised concerns around confidentiality and privacy of healthcare information. Female Veterans expressed a desire for gender-sensitive care and an interest in complementary and alternative medicine. CONCLUSIONS: The findings and specific recommendations from this study can improve existing remote management programs and inform the design of future efforts.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".