Moving the Agenda Forward Together: Innovating Indigenous Primary Care in Alberta, Strategic Event Report 2016
Bibliographic record
Abstract
Practitioners, policy-makers, and planners in Alberta note that quality primary care for Indigenous people is undermined by significant structural gaps and deficiencies. In spite of some recent innovations, Alberta seems to lag behind similar jurisdictions, such as Ontario and British Columbia, in mobilizing structures to improve primary care delivery that is culturally safe, acceptable and equitable for Indigenous people. In January 2016, the University of Calgary’s Department of Family Medicine in the Cumming School of Medicine convened Indigenous community members and leaders, as well as provincial health system leaders, primary care practitioners and researchers near Calgary, Alberta to share and explore these barriers. The aim was to optimize the potential for creative change stirred in the province following provincial and federal elections in 2015 that shifted policy landscapes. This report highlights innovations shared from other jurisdictions in Canada, and the opportunities that these innovations present to the Alberta context. The event convened 65 Alberta-based stakeholders, with guest presenters from across Canada, from the: Vancouver Native Health Society (VNHS); Tui’kn Partnership in Cape Breton, Nova Scotia; and Cree Board of Health and Social Services of James Bay (CBHSSJB), Quebec. In small groups, presenters provided overviews of innovations in primary care developed by their organizations, including big picture strategies, and barriers/facilitators to innovation. Small group participants then reflected and explored how such innovations might make sense or be translated into Alberta’s diverse Indigenous contexts. Guest speakers and facilitators from Alberta Health Services (AHS), Health Canada, the Public Health Agency of Canada (PHAC), Siksika Health Services, and the Indigenous Physicians Association of Canada (IPAC) helped to integrate knowledge and experiences shared. The models presented were broadly grouped into urban, reserve, and system-level innovations. A physician lead and Elder from the VNHS presented their agency’s VIP Elder program that offers spiritual and emotional support to interested clientele; the health director from Eskasoni First Nation’s health centre shared the story of forging the Tui’kn Partnership with neighbouring communities for ownership and control of health data for improved care; and a lead physician gave an overview of the CBHSSJB’s life-cycle approach structuring all aspects of care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".