Health Policy for Dialysis Care in Canada and the United States
Bibliographic record
Abstract
Contemporary dialysis treatment for chronic kidney failure is complex, is associated with poor clinical outcomes, and leads to high health costs, all of which pose substantial policy challenges. Despite similar policy goals and universal access for their kidney failure programs, the United States and Canada have taken very different approaches to dealing with these challenges. While US dialysis care is primarily government funded and delivered predominantly by private for-profit providers, Canadian dialysis care is also government funded but delivered almost exclusively in public facilities. Differences also exist for regulatory mechanisms and the policy incentives that may influence the behavior of providers and facilities. These differences in health policy are associated with significant variation in clinical outcomes: mortality among patients on dialysis is consistently lower in Canada than in the United States, although the gap has narrowed in recent years. The observed heterogeneity in policy and outcomes offers important potential opportunities for each health system to learn from the other. This article compares and contrasts transnational dialysis-related health policies, focusing on key levers including payment, finance, regulation, and organization. We also describe how policy levers can incentivize favorable practice patterns to support high-quality/high-value, person-centered care and to catalyze the emergence of transformative technologies for alternative kidney replacement strategies.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".