Access to health care among status Aboriginal people with chronic kidney disease
Bibliographic record
Abstract
BACKGROUND: Ethnic disparities in access to health care and health outcomes are well documented. It is unclear whether similar differences exist between Aboriginal and non-Aboriginal people with chronic kidney disease in Canada. We determined whether access to care differed between status Aboriginal people (Aboriginal people registered under the federal Indian Act) and non-Aboriginal people with chronic kidney disease. METHODS: We identified 106 511 non-Aboriginal and 1182 Aboriginal patients with chronic kidney disease (estimated glomerular filtration rate less than 60 mL/min/1.73 m(2)). We compared outcomes, including hospital admissions, that may have been preventable with appropriate outpatient care (ambulatory-care-sensitive conditions) as well as use of specialist services, including visits to nephrologists and general internists. RESULTS: Aboriginal people were almost twice as likely as non-Aboriginal people to be admitted to hospital for an ambulatory-care-sensitive condition (rate ratio 1.77, 95% confidence interval [CI] 1.46-2.13). Aboriginal people with severe chronic kidney disease (estimated glomerular filtration rate < 30 mL/min/1.73 m(2)) were 43% less likely than non-Aboriginal people with severe chronic kidney disease to visit a nephrologist (hazard ratio 0.57, 95% CI 0.39-0.83). There was no difference in the likelihood of visiting a general internist (hazard ratio 1.00, 95% CI 0.83-1.21). INTERPRETATION: Increased rates of hospital admissions for ambulatory-care-sensitive conditions and a reduced likelihood of nephrology visits suggest potential inequities in care among status Aboriginal people with chronic kidney disease. The extent to which this may contribute to the higher rate of kidney failure in this population requires further exploration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".