Using the Fraser’s Triangle to Examine the Social Determinants of Health in Indigenous People across Northwestern Ontario
Bibliographic record
Abstract
Indigenous populations in Northwestern Ontario face persistent and disproportionate health challenges, shaped by systemic barriers and legacies of colonialism. These disparities stem from social, political, and economic conditions that influence health outcomes and access to care. The social determinants of health (SDH) provide a crucial framework for understanding these inequities, as they reflect the broader conditions influencing well-being. This study applies Fraser’s model—centering on recognition, redistribution, and representation—to examine how structural inequalities, cultural marginalization, and political exclusion contribute to Indigenous health inequities [4]. By integrating Fraser’s framework, this research underscores the need for culturally relevant healthcare solutions that prioritize social justice and Indigenous self-determination. This study emphasizes the significance of decolonizing healthcare systems and the necessity for policies that incorporate Indigenous perspectives and lived experiences. By critically examining existing structures and frameworks, the analysis contributes to ongoing efforts aimed at achieving equitable and culturally responsive healthcare for Indigenous populations in Northwestern Ontario.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".