Developing community-driven quality improvement initiatives to enhance chronic disease care in Indigenous communities in Canada: the FORGE AHEAD program protocol
Bibliographic record
Abstract
BACKGROUND: Given the dramatic rise and impact of chronic diseases and gaps in care in Indigenous peoples in Canada, a shift from the dominant episodic and responsive healthcare model most common in First Nations communities to one that places emphasis on proactive prevention and chronic disease management is urgently needed. METHODS: The Transformation of Indigenous Primary Healthcare Delivery (FORGE AHEAD) Program partners with 11 First Nations communities across six provinces in Canada to develop and evaluate community-driven quality improvement (QI) initiatives to enhance chronic disease care. FORGE AHEAD is a 5-year research program (2013-2017) that utilizes a pre-post mixed-methods observational design rooted in participatory research principles to work with communities in developing culturally relevant innovations and improved access to available services. This intensive program incorporates a series of 10 inter-related and progressive program activities designed to foster community-driven initiatives with type 2 diabetes mellitus as the action disease. Preparatory activities include a national community profile survey, best practice and policy literature review, and readiness tool development. Community-level intervention activities include community and clinical readiness consultations, development of a diabetes registry and surveillance system, and QI activities. With a focus on capacity building, all community-level activities are driven by trained community members who champion QI initiatives in their community. Program wrap-up activities include readiness tool validation, cost-analysis and process evaluation. In collaboration with Health Canada and the Aboriginal Diabetes Initiative, scale-up toolkits will be developed in order to build on lessons-learned, tools and methods, and to fuel sustainability and spread of successful innovations. DISCUSSION: The outcomes of this research program, its related cost and the subsequent policy recommendations, will have the potential to significantly affect future policy decisions pertaining to chronic disease care in First Nations communities in Canada. TRIAL REGISTRATION: Current ClinicalTrial.gov protocol ID NCT02234973 . Date of Registration: July 30, 2014.
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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 | 0.064 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.003 | 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".