Urban Aboriginal mobility in Canada: Examining the association with health care utilization
Bibliographic record
Abstract
In recent decades, Indigenous peoples across the globe have become increasingly urbanized. Growing urbanization has been associated with high rates of geographic mobility between rural areas and cities, as well as within cities. In Canada, over 54 percent of Aboriginal peoples are urban and change their place of residence at a higher rate than the non-Aboriginal population. High rates of mobility may affect the delivery and use of health services. The purpose of this paper is to examine the association between urban Aboriginal peoples' mobility and conventional (physician/nurse) as well as traditional (traditional healer) health service use in two distinct Canadian cities: Toronto and Winnipeg. Using data from Statistics Canada's 2006 Aboriginal Peoples Survey, this analysis demonstrates that mobility is a significant predisposing correlate of health service use and that the impact of mobility on health care use varies by urban setting. In Toronto, urban newcomers were more likely to use a physician or nurse compared to long-term residents. This was in direct contrast to the effect of residency on physician and nurse use in Winnipeg. In Toronto, urban newcomers were less likely to use a traditional healer than long-term residents, indicating that traditional healing may represent an unmet health care need. The results demonstrate that distinct urban settings differentially influence patterns of health service utilization for mobile Aboriginal peoples. This has important implications for how health services are planned and delivered to urban Aboriginal movers on a local, and potentially global, scale.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".