947-P: Needs Assessment for a Collaborative Patient-Centered Digital Health Application for Pediatric Diabetes Care in British Columbia
Bibliographic record
Abstract
There has been a rapid rise in digital health applications to support diabetes self-management to improve patient-centered outcomes such as engagement and satisfaction. TrustSphere is a collaborative single point of access digital health application that has been developed using principles of co-creation and participatory design with health care providers (HCP) and patients/families. Our objective is to describe HCP perspectives on current challenges and ideas for digital application features. A survey was distributed to pediatric diabetes HCP across British Columbia, Canada (n=50; 36% diabetes nurse educators, 24% nursing support services, 18% pediatricians, 12% dietitians, 8% pediatric endocrinologists) . HCP identified access to mental health support (84%) , diabetes management technologies (53%) , clinical information (39%) , and support for diabetes care at school (35%) as key challenges. Qualitative data revealed struggles with staying up to date with diabetes technology and empowering patients to self-manage their diabetes between visits. HCP reported TrustSphere should include information on the type of insulin regimen (87%) , insulin doses & pump settings (76%) and patient-centered goals (41%) as well as integration of glucose meter (91%) , continuous glucose monitor (CGM) (93%) , and insulin pump data (91%) . Overall, HCP felt an integrated digital platform would greatly simplify their care for children with T1D (47%) . HCP perspectives were integrated into TrustSphere’s minimal viable product (MVP) . Via real-time connections, glucometer, CGM, and pump data have been integrated into the platform. Insulin doses, patient tasks, and management recommendations can be collaboratively entered by patients or HCP, optimizing personalized care. Educational resources and a mindfulness app to support mental health can also be accessed via TrustSphere. Next steps are a pilot study of the TrustSphere MVP with pediatric T1D patients. Disclosure F.S.Abdulhussein: None. S.Amed: Advisory Panel; Insulet Corporation, Lilly, Sanofi, Research Support; Dexcom, Inc., Novo Nordisk, Speaker's Bureau; Abbott Diabetes. S.Pinkney: None. Funding Canadian Digital TechnologySuperclusterUniversity of British Columbia
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".