Power to Empower: \nDesigning for Type 2 Diabetes
Bibliographic record
Abstract
Abstract \nThe growing prevalence of people living with Type 2 Diabetes (PLWT2D) and the increase in the magnitude of associated comorbidities continue to impact people across Canada leading to severe health problems with no signs of slowing down. Despite significant investments in research, public health interventions, clinical care and new treatments and technologies, Canada ranks 10th among the 17 peer countries. (OECD Health, 2023) in Diabetes prevalence. \nCanada has a low physician-to-patient ratio and many Canadians do not have access to a family doctor. Canada has 2.7 physicians per 1,000 people compared to the OECD average of 3.5 physicians per 1,000 people (OECD Health, 2023). Most People living with Type 2 Diabetes are under the care of primary care practitioners, who are experiencing unprecedented burnout, an increase in unpaid administrative work, and limited time to see the overflowing numbers of patients in their practice. \nThe implications of the increase in prevalence of Type 2 Diabetes and less access to primary care practitioners to provide care, creates concern about the future of population health and the capabilities of health care systems to meet the demands. \nTo better understand why the problem persists, our research focused on what creates delays in recommendations for care from healthcare providers, and what creates delays in the adoption of recommendations for care by people living with Type 2 Diabetes. \nBold intervention strategies are required to change the trajectory of Type 2 diabetes prevalence and in this paper, we make a case for how design offers new perspectives on problem solving within the scope of providers of care and people living with Type 2 Diabetes.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".