Healthcare utilization for arthritis by indigenous populations of Australia, Canada, New Zealand, and the United States: A systematic review ☆
Bibliographic record
Abstract
PURPOSE: Indigenous populations of Australia, Canada, New Zealand, and the United States of America (USA) experience a higher prevalence of arthritis conditions. Differences in clinical outcomes and mortality may reflect healthcare service use inequities. The objective of this study was to summarize healthcare service use patterns described in the existing literature in order to identify gaps and inform strategies to limit the pronounced negative impact of arthritis on Indigenous populations. METHODS: Medline, EMBASE, CINAHL, and Indigenous-specific electronic databases (to June 2015) were used to identify cohort, case-control and cross-sectional studies describing healthcare service use by Indigenous populations with specified inflammatory arthritis, osteoarthritis, or rheumatic disease conditions. We extracted information on the study setting and methodology, primary outcome and assessed study quality, and risk of bias. RESULTS: In total, 19 studies were identified describing three types of healthcare service use: physician visits, hospitalizations, and surgeries. In Canada and New Zealand, Indigenous populations had 36-51% fewer visits to specialists than the non-Indigenous population. Indigenous populations in Canada, New Zealand, and the USA had 37-300% more hospitalizations due to arthritis complications than the non-Indigenous population. Indigenous populations in Australia, Canada, and New Zealand had 27-85% fewer arthroplasties for osteoarthritis than the non-Indigenous population. CONCLUSIONS: Indigenous populations had higher hospitalization rates but lower use of specialized services for arthritis conditions. Strategies to improve access to specialized arthritis services might reduce health outcome inequities.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".