Addressing the gaps in diabetes care in first nations communities with the reorganizing the approach to diabetes through the application of registries (RADAR): the project protocol
Bibliographic record
Abstract
BACKGROUND: Type-2 diabetes rates in First Nations communities are 3-5 times higher than the general Canadian population, resulting in a high burden of disease, complications and comorbidity. Limited community nursing capacity, isolated environments and a lack of electronic health records (EHR)/registries lead to a reactive, disorganized approach to diabetes care for many First Nations people. The Reorganizing the Approach to Diabetes through the Application of Registries (RADAR) project was developed in alignments with federal calls for innovative, culturally relevant, community-specific programs for people with type-2 diabetes developed and delivered in partnership with target communities. METHODS: RADAR applies both an integrated diabetes EHR/registry system (CARE platform) and centralized care coordinator (CC) service that will support local healthcare. The CC will work with local healthcare workers to support patient and community health needs (using the CARE platform) and build capacity in best practices for type-2 diabetes management. A modified stepped wedge controlled trial design will be used to evaluate the model. During the baseline phase, the CC will work with local healthcare workers to identify patients with type-2 diabetes and register them into the CARE platform, but not make any management recommendations. During the intervention phase, the CC will work with local healthcare workers to proactively manage patients with type-2 diabetes, including monitoring and recall of patients, relaying clinical information and coordinating care, facilitated through the shared use of the CARE platform. The RE-AIM framework will provide a comprehensive assessment of the model. The primary outcome measure will be a 10% improvement in any one of A1c, BP, or cholesterol over the baseline values. Secondary endpoints will address other diabetes care indicators including: the proportion of clinical measures completed in accordance with guidelines (e.g., foot and eye examination, receipt of vaccinations, smoking cessation counseling); the number of patients registered in CARE; and the proportion of patients linked to a health services provider. The cost-effectiveness of RADAR specific to these communities will be assessed. Concurrent qualitative assessments will provide contextual information, such as the quality/usability of the CARE platform and the impact/satisfaction with the model. DISCUSSION: RADAR combines innovative technology with personalized support to deliver organized diabetes care in remote First Nations communities in Alberta. By improving the ability of First Nations to systematically identify and track diabetes patients and share information seamlessly an overall improvement in the quality of clinical care of First Nations people living with type-2 diabetes on reserve is anticipated. TRIAL REGISTRATION: ISRCTN study ID ISRCTN14359671 , retrospectively registered October 7, 2016.
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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 | 0.113 | 0.084 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.044 | 0.011 |
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".