Parallel systems in healthcare: Addressing Indigenous health equity in Canada
Bibliographic record
Abstract
The Canadian public healthcare system faces significant challenges in performance. While the formal healthcare system addresses funding, access and policy, there is a critical need to prioritise the informal system of community-oriented networks. This integration aligns with the World Health Organization's primary health care approach, emphasising a whole-of-society strategy for health equity. Canada's healthcare, harmonised through the Canada Health Act of 1984, focuses on need over ability to pay. Despite successes, the system struggles with social determinants of health and widening health inequities, especially among Indigenous peoples. Historical policies of forced assimilation have led to poor health outcomes and lower life expectancies for Indigenous populations. The Truth and Reconciliation Commission's Calls to Action stress removing barriers at multiple levels to improve Indigenous health. Indigenous perspectives on health, emphasising holistic wellness, contrast with Western healthcare's acute-illness focus. The emergence of parallel systems, informal networks within healthcare, reflects dissatisfaction with traditional approaches. Recognising the parallel system within Indigenous health, as proposed, can transform healthcare to better meet population needs. Systems mapping of Indigenous PHC in Alberta revealed numerous entities providing healthcare access, highlighting the importance of adequately funding and integrating these parallel systems to advance health equity.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".