Investigating the Role of Telemedicine in Managing Type 2 Diabetes in Rural Adults
Bibliographic record
Abstract
Introduction: Type 2 diabetes (T2D) is a growing concern in Canada and around the world leading to life-threatening complications if left untreated. Rural populations are disproportionately affected due to the inaccessibility of specialist care and diabetes self-management education (DSME). The use of telemedicine for the management of chronic disease is a growing field of research in recent years. This literature review investigates the role of teleconsultation with an endocrinologist for managing T2D in rural adults in North America. Methods: A literature search on PubMed using appropriate search terms was conducted. Studies in rural North American communities involving teleconsultation with an endocrinologist and a component of DSME were included. The primary outcome measured was HbA1c. Results: A total of six articles met inclusion criteria. No Canadian studies were identified. All studies found a significant decrease in the HbA1c measures after the telemedicine intervention. Discussion: The findings of this literature review suggest that telemedicine is effective at reducing HbA1c measures in North American adults with T2D. However, given the small sample sizes and non-randomised control trials, further research is needed, especially in rural Canadian populations, to determine the role of telemedicine for T2D management and its implementation in rural Canada. Telemedicine has many advantages, including increased accessibility to specialist care and reduced travel costs to urban centres. Barriers to telemedicine include implementation difficulties, access to the internet and technology and security concerns. Conclusions: Telemedicine for managing T2D is shown to be effective in reducing HbA1c measures in North American rural adults. However, further primary evidence is required to determine a causative effect.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".