Increasing Access to Chronic Disease Self-Management Programs in Rural and Remote Communities Using Telehealth
Bibliographic record
Abstract
OBJECTIVE: This study examined whether a telehealth chronic disease self-management program (CDSMP) would lead to improvements in self-efficacy, health behaviors, and health status for chronically ill adults living in Northern Ontario, Canada. Two telehealth models were used: (1) single site, groups formed by participants at one telehealth site; and (2) multi-site, participants linked from multiple sites to form one telehealth group, as a strategy to increase access to the intervention for individuals living in rural and remote communities. SUBJECTS AND METHODS: Two hundred thirteen participants diagnosed with heart disease, stroke, lung disease, or arthritis attended the CDSMP at a preexisting Ontario Telemedicine Network studio from September 2007 to June 2008. The program includes six weekly, peer-facilitated sessions designed to help participants develop important self-management skills to improve their health and quality of life. Baseline and 4-month follow-up surveys were administered to assess self-efficacy beliefs, health behaviors, and health status information. Results were compared between single- and multi-site delivery models. RESULTS: Statistically significant improvements from baseline to 4-month follow-up were found for self-efficacy (6.6±1.8 to 7.0±1.8; p<0.001), exercise behavior, cognitive symptom management, communication with physicians, role function, psychological well-being, energy, health distress, and self-rated health. There were no statistically significant differences in outcomes between single- and multi-site groups. CONCLUSIONS: Improvements in self-efficacy, health status, and health behaviors were equally effective in single- and multi-site groups. Access to self-management programs could be greatly increased with telehealth using single- and multi-site groups in rural and remote communities.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".