Capacity of Kidney Care in Canada: Identifying Barriers and Opportunities
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) is a significant health problem in Canada. Understanding the capacity of the Canadian health-care system to deliver kidney care is important to provide optimal care. OBJECTIVE: To compare Canada's position in relation to countries of similar economic standing. DESIGN: Cross-sectional electronic survey. SETTING: Member countries of the Organisation for Economic Co-operation and Development (OECD) that participated in the survey. PARTICIPANTS: Nephrologists, other physicians, policymakers, and other professionals with relevant expertise in kidney care. MEASUREMENTS: Not applicable. METHODS: A survey administered by the International Society of Nephrology assessed the global capacity of kidney care delivery. Data from participating OECD countries were analyzed using descriptive statistics to compare Canada's position. RESULTS: Of the participating countries, most funded kidney care services (non-medication) by government (transplantation: 85%, dialysis: 81%, acute kidney injury (AKI): 77%). Most countries covered medication. Canada reported a public funding model for kidney services and a mix of public and private sources for medication. Nephrologists and nephrology trainee densities were lower in Canada compared to the median (15.33 vs. 25.82 and 1.74 vs. 3.94, respectively). CKD was recognized as a health priority in five countries, but not in Canada. Registries for CKD did not exist in most (24/26) countries. Canada followed a national strategy for noncommunicable diseases, but this was not specific to CKD care, dialysis, or transplantation. LIMITATIONS: Risks of recall bias or social desirability bias are present. Differences in a number of factors could influence discrepancies among countries and were not explored. Responses reflected the existence of practices, policies, and strategies, and may not necessarily describe action or impact. Capacity of care is not equal across all regions and provinces within Canada; however, the findings are reported on a national level and therefore may not appropriately address variability. CONCLUSIONS: This study describes the capacity for kidney care at a national level within the context of the Canadian health system. The Canadian health-care system is well funded by the government; however, there are areas that could be improved to increase the optimization of kidney care provided.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".