An Investigation into the Role for a Pervasive technology Solution to support Gestational Diabetes Self-care
Bibliographic record
Abstract
Background: Diabetes mellitus [diabetes] is one of the leading chronic diseases affecting Australians and its prevalence continues to rise at an exponential rate with an estimated 275 Australians developing diabetes daily [1-3]. Globally, the total number of diabetic patients worldwide is estimated to rise to 366 million in 2030 from 171 million in 2000. Early detection and management of diabetes is essential. A critical treatment imperative is to provide patients with diabetes appropriate monitoring to enable better assessment and control of blood glucose, and to prevent further complications. As yet, there is no universal cure for diabetes, so what is vital for both patients and the healthcare system is prudent and superior management. Hence, we contend that a pervasive technology solution that can enable ubiquitous (anytime, anywhere, anyplace) monitoring of diabetic patients while also continuously educating them should be a prudent part of such a management program. Objective: To investigate the benefits of a pervasive technology to facilitate and enable superior self-care for patients suffering from GDM (gestational diabetes) and to answer following research questions: • Can the INET(technology) solution(a solution developed in Canada and successfully trialled in Canada and US) facilitate superior GDM self-management in an Australian context? • What are the benefits and suitability of such a pervasive technology solution to GDM self-care? • What are the key barriers and facilitators for the application of a pervasive technology solution to support GDM patient care? Method: The research will subscribe to the established techniques adopted by Wickramasinghe and Goldberg to date; ie the AMR methodology. In addition, a cross over style RCT(randomised control trial) will be utilised which means that the control group will at a predetermined time convert to using the technology solution while the intervention group will at this same point in time then revert to the traditional solution. This strategy is deemed appropriate in studies of this nature so that it is possible for patients to compare with /without technology scenarios. Established qualitative and quantitative techniques will be employed to analyse the collected data. Specifically, from the qualitative data, thematic analysis will be performed in accordance with standard approaches described by Boyatzis and Kvale, while regression techniques and exploratory data mining techniques will for the major focus for the quantitative part. Results: The results to date of this research in progress include the establishment of the appropriate delivery framework and web-based conceptual model. A key aspect of this conceptual model is that it emphasises sustainability and long term support for patients. Given the nature of diabetes this is an important aspect. Conclusions: Based on the success and findings of using a pervasive technology solution to support diabetes self-care in both Canada and US, we have attempted to adapt such a solution into the Australian context. Like all countries today, Australia also has an increasing number of individuals diagnosed with diabetes and this statistic is predicted to grow exponentially in the next decades which in turn places a significant on patients, their families, the community and the healthcare system. We have chosen to initially focus on patients suffering with GDM as this segment of the population is technology savvy and therefore should embrace a technology support solution readily. The next step is to now run the pilot studies. While clinically, no major differences exist in the treatment of GDM in North America and Australia, given fundamental differences in the respective healthcare systems i.e., Australia has a two-tier healthcare system, demonstrating the benefits of this solution in an Australian healthcare context will serve to underscore the true pervasiveness of this solution. []
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".