Aboriginal Peoples' Mobility and Health in Urban Canada: Traversing Ideological and Geographical Boundaries
Bibliographic record
Abstract
In recent decades, the Aboriginal population in Canada has become increasingly urbanized. Urbanization has been accompanied by high rates of mobility between reserve/rural and urban areas, as well as within cities. While research has documented Aboriginal peoples’ mobility rates, little attention has been given to mobility experiences, and an understanding of the socio-political and historic context in which mobility is set remains underdeveloped. Furthermore, little is known about the impact of mobility on movers’ holistic health (i.e., physical, mental, emotional, spiritual), and while research has suggested that mobility may impact access to urban social and health services, little is known in this area. The objectives of this dissertation are therefore to examine: the broader motivations that shape mobility, the link between mobility and health as well as service use, and to produce a more comprehensive understanding of the relationship between service providers and movers. These objectives are addressed using multiple methods. Quantitative analyses of the 2006 Aboriginal Peoples Survey identified mobility as a significant correlate of conventional (physician/nurse) and traditional (traditional healer) health care use. In order to explore nuanced links between mobility, health, and urban service delivery, a collaborative, community-based research relationship was established with an urban Aboriginal-led organization and 46 in-depth, semi-structured interviews were conducted with Aboriginal service providers, non-Aboriginal service providers, and urban Aboriginal movers in the city of Winnipeg, Manitoba, Canada. These research findings reveal the importance of service delivery that actively supports urban Aboriginal movers, and demonstrates the relationship between mobility and holistic health as well as service access in urban areas. Furthermore, current scales of service delivery are found to be insufficient for meeting the needs of mobile urban Aboriginal populations. Despite these findings, urban Aboriginal movers are maintaining important networks of support between their points of origin and destination, and are creating new spaces of engagement within cities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.037 | 0.017 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".