Bibliographic record
Abstract
An examination of the barriers facing Indigenous people within the healthcare system from the perspective of an empathetic settler physician After leaving her medical practice in Pennsylvania in 2011, Jarol Boan returned to her childhood home in Saskatchewan, Canada to practice medicine. There she found a healthcare system struggling with preventable chronic diseases and institutional racism. Shocked by the high rate of preventable diseases in her patients, Boan realized that a paternalistic deficit model does not support Indigenous communities. Through working to provide medical services in Indigenous communities and learning firsthand from her Indigenous patients, Boan embarked on a road to enlightenment and reconciliation. In The Medicine Chest , Boan exposes the healthcare disparities in a country that prides itself on an equitable healthcare system and examines the devastating effects of diabetes, the myth of “the drunken Indian,” the inner workings of hospitals, Missing and Murdered Indigenous Women and Girls, epidemics on reserves, and residential school trauma. Exploring the intersectionality of common diseases and social determinants of health gained from her experience of caring for Indigenous patients, Boan weaves historical data, comments on health policy, and jurisdictional gaps into the narrative while investigating how Canada’s healthcare system is failing those most in need.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.131 | 0.031 |
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".