An Investigation into the Role for a Pervasive technology Solution to support Gestational Diabetes Self-care
Notice bibliographique
Résumé
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. []
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».