Critically examining health complexity experienced by urban Indigenous peoples in Canada by exploring the factors that allow health complexity to persist: a qualitative study of Indigenous patients in Calgary, Alberta
Bibliographic record
Abstract
OBJECTIVES: This study aims to identify and critically examine the components of health complexity, and explore the factors that allow it to exist, among urban Indigenous peoples in Canada. DESIGN: Qualitative exploration with relational conversations. SETTING: Calgary, Alberta, Canada. PARTICIPANTS: A total of nine urban Indigenous patients were recruited from a multidisciplinary primary healthcare clinic that serves First Nations, Métis and Inuit peoples. Recruitment and data collection took place between September and November 2021. RESULTS: Thematic analysis revealed three main themes, namely: sources of health complexity, psychological responses to adversity, and resilience, strengths, and protective factors. Key sources of health complexity arose from material resource disparities and adverse interpersonal interactions within the healthcare environment, which manifest into psychological distress while strengths and resilience emerged as protective factors. CONCLUSION: The healthcare system remains inapt to address complexity among urban Indigenous peoples in Canada. Healthcare violence experienced by Indigenous peoples only further perpetuates health complexity. Future clinical tools to collect information about health complexity among urban Indigenous patients should include questions about the factors defined in this study.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.018 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| 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".