The Impact of Secure Messaging in the Treatment of Patients With Diabetes Within a Primary Care Setting: Protocol for a Scoping Review (Preprint)
Bibliographic record
Abstract
BACKGROUND Diabetes—a high-burden chronic disease—requires lifetime active management involving the use of different tools and health care resources to improve patient health outcomes. Recent studies have demonstrated promising results regarding the impact of the use of virtual care technology on the treatment of chronic diseases, such as diabetes. However, it is unclear whether the use of technologies, such as secure messaging, improves the quality of care and reduces diabetes-related costs to the health care system. OBJECTIVE The purpose of our scoping review is to explore what is known about the use of secure messaging in the treatment of diabetes within the primary care setting and how its impact has been assessed from the patient and health system perspectives. Our review aims to understand to what extent secure messaging improves the quality of diabetes care. METHODS Our scoping review will follow the 6-step Arksey and O’Malley methodological framework, as well as the Joanna Briggs Institute methodology for scoping reviews and their recommended tools. The tools to guide the development and reporting of the review in a structured way will include the Population, Concept, and Context framework and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines and checklist. The search strategy was developed iteratively in collaboration with a professional information specialist. Furthermore, a peer review of electronic search strategies was also conducted by an independent, third-party, professional information specialist. A systematic literature search will be conducted against databases, including Ovid MEDLINE ALL, Embase, APA PsycINFO, Cochrane Library on Wiley, CINAHL on EBSCO, and PubMed. Grey literature sources will also be searched for relevant literature. Literature on the use of secure messaging in the treatment of diabetes (types 1 and 2) within a primary care setting will be included. Two reviewers will review the literature based on the inclusion criteria in the following two steps: (1) title and abstract review and (2) full-text review. Discrepancies will be discussed to reach consensus where possible; otherwise, a third reviewer will resolve the dispute. RESULTS The results and a final report are expected to be completed and submitted to a peer-reviewed journal in 6 months. CONCLUSIONS The review will examine existing literature to identify the impact of secure messaging in diabetes treatment within primary care settings. Research gaps will also be identified to determine if there is a need for further studies. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/42339
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.090 | 0.110 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.017 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.100 | 0.020 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".