Structural Racism as an Ecosystem: An Exploratory Study on How Structural Racism Influences Chronic Disease and Health and Wellbeing of First Nations in Canada
Bibliographic record
Abstract
Indigenous peoples in Canada experience disproportionately higher rates of chronic disease than their non-Indigenous counterparts. Previous research has identified structural racism as a powerful determinant of health and wellbeing. Mounting evidence demonstrates that First Nations are disproportionately over-represented, compared to other Canadians, in several domains that have been used to measure structural racism in other countries. Despite growing concern of the impact of structural racism on health, there remains little empirical evidence on the impact structural racism has on chronic disease health outcomes of First Nations. This qualitative study examines the complex and intersecting ways in which structural racism can influence chronic disease health outcomes and the overall health and wellbeing of First Nations in Canada. In-depth semi-structured interviews were conducted with twenty-five participants, including subject matter experts in health, justice, education, child welfare, politics, and researchers in racism scholarship and First Nations who have lived experience with a chronic condition(s). Thematic analysis was used to analyze the data collected. Six themes on how structural racism influences chronic disease and the health of First Nations were identified: (1) multiple and intersecting pathways; (2) systems of failure, harm, and indifference; (3) impacts on access to healthcare; (4) colonial policies of structural deprivation; (5) increased risk factors for chronic disease and poor health; and (6) structural burden leading to individual-level outcomes. Structural racism creates an ecosystem that negatively impacts chronic diseases and the health of First Nations. The findings illuminate how structural racism can have micro-level influences at an individual level and can influence one's chronic disease journey and progression. Recognizing how structural racism shapes our environments may help to catalyze a shift in our collective understanding of the impact of structural racism on health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".