Arthritis, aches and pains, and arthritis services : experiences from within an urban First Nations community
Bibliographic record
Abstract
This dissertation explored the experiences of health and healthcare reported by members of an urban First Nations community who had arthritis and the ways in which arthritis health services were aligned (or not aligned) with these experiences. Using a community-based, participatory design, grounded in decolonizing methodologies and ethnographic methods, this study had two research fields that related to the research questions. Study activities in one research field included intensive immersion in a First Nations community over a period of three years, and interviews with 24 community members. In the second field, which included three arthritis services settings, study activities involved approximately 100 hours of immersion and interviews with 30 healthcare professionals. The analysis of community-based data revealed that experiences of ongoing arthritis/pain and social suffering were inextricably linked to and underpinned by the social and historical context of life in the community. Most, but not all, community participants were reluctant users of health services, largely related to prior negative experiences utilizing health services. The organization and delivery of arthritis health services, shaped by dominant healthcare discourses, were not well aligned with the experiences of First Nations peoples with arthritis; rationing and biomedical discourses limited the ability of the system to be responsive to social contexts, and culturalist and self-management discourses served to deflect healthcare professionals’ attention away from the role that social and material life conditions played in shaping the experiences of First Nations individuals living with arthritis. Amongst arthritis health services leaders and professionals there was a sincere desire to provide effective, quality care to all people with arthritis. Creating more opportunities for social/critical knowledge to be present in health services settings could go a long way towards improving the alignment of arthritis services with arthritis experiences of First Nations peoples.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".