Balancing Equity Issues in Health Systems: The Example of Vancouver Coastal Health
Bibliographic record
Abstract
Introduction Canada’s public health system is committed to providing necessary healthcare services to all Canadians. This principle is so simple – as well as morally and practically appealing – that it has become embedded as a fundamental part of what it means to be Canadian. It is, of course, much more complex to follow through on the principle than to support its philosophy. An ideal world would be one in which the need or demand for health services were fully matched by resources and the system’s capacity to deliver desired activities. However, as we all know this ideal state does not exist in any public healthcare system in the developed world. This gap between ideals and reality gives rise to the challenge of allocating scarce resources so as to fulfill “most-needed” services. At the health system delivery level, this drama is played out every day – nowhere more so than in the territory overseen by Vancouver Coastal Health (VCH). In this region, Canada’s highest per-capita income postal code lies within a few kilometres of the Downtown Eastside, the country’s lowest percapita income postal code. VCH staff decisions about who receives care and how much, and – perhaps more importantly – who does not, are in constant and stark relief.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.018 |
| Scholarly communication | 0.014 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 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".