Bibliographic record
Abstract
In 2019, Canadian First Nations activist David Dennis challenged the 6-month alcohol abstinence requirement when he was denied placement on the liver transplant list. While Mr. Dennis was eventually placed on the waitlist, he passed away before receiving a transplant. As Canadian transplant programs seek to reduce healthcare inequities experienced by Indigenous patients—First Nations, Métis, and Inuit—this analysis sheds much needed light on the inconvenient truths of settler colonialism and the need for impactful reconciliation in Canadian healthcare. This analysis examines Dennis’ activism and his argument about the inequities in transplant medicine experienced by Indigenous patients. Dennis’ activism highlighted how the Canadian healthcare systems underserve Indigenous populations, including in transplant medicine; fails to address racial biases within healthcare; and does little to consider and address negative inter-generational determinants of health that perpetuate poor health outcomes for Indigenous populations. This analysis provides direction to decision makers in transplant and additions medicine, to Ministries of Health, and to First Nations, Métis, and Inuit healthcare leaders for improved healthcare for Indigenous patients living with alcohol-related liver disease. Without concerted and coordinated efforts to address the needs of this patient population, we will continue to see rapid deterioration of their health status and high rates of mortality will persist among this patient population.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.014 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".