Can mHealth, when monitored by a primary care provider, maintain hemoglobin A1c values in target range with adolescents between the ages of 10-19 diagnosed with type 1 diabetes mellitus?
Bibliographic record
Abstract
Diabetes mellitus is an international epidemic affecting millions of individuals worldwide. In Canada, an estimated 3.4 million individuals are living with diabetes mellitus— approximately 9.3 percent of the total population (Canadian Diabetes Association, 2018). A high prevalence of diabetes mellitus comes with a substantial cost; the direct annual cost associated with diabetes mellitus is expected to reach 3.1 billion dollars by 2020 (Bilandzic & Rosella, 2017). The Canadian Pediatric Society recognizes that there are currently 33,000 children and adolescents aged 5-18 years old living with type 1 diabetes mellitus (T1DM) (as cited in the Diabetic Children’s Foundation, 2018). As children and adolescents with T1DM are in the process of developing physically and psychologically, they are at an increased risk of developing complications of diabetes that require short to long term attention and monitoring. The utilization of mobile technology to provide healthcare services is commonly referred to as mHealth. Such technology offers an opportunity to address the challenges of chronic disease management with this technology- intelligent population (Kitsiou, Paré, Jaana, & Gerber, B. 2017). As a component of the Master of Science in Nursing- Nurse Practitioner program at the University of Northern British Columbia, the following is an integrative review to answer the research question: Can mHealth, when monitored by a primary care provider, maintain hemoglobin A1c values in target range with adolescents between the ages of 10-19 diagnosed with type 1 diabetes mellitus?
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".