A New Way Forward: Recognizing the Importance of HIV in Controlling Tuberculosis among Canada’s Indigenous Population
Bibliographic record
Abstract
Canada’s indigenous population (which includes the First Nations, Inuit, and Metis) suffers from startling health inequities that have been largely attributed to the persisting effects of colonization leading to poverty, overcrowding, and unemployment (Macaulay, 2009). As important social determinants of health, these conditions have played a critical role in the progression of both communicable and non-communicable disease epidemics amongst the indigenous population, including tuberculosis (TB) and human immunodeficiency virus (HIV). The prevalence of TB and HIV within the indigenous population is approximately 34 times and 2 times greater than in the non-indigenous population, respectively (PHAC, 2012; PHAC, 2016). The government of Canada has responded to these striking discrepancies by implementing national HIV and TB prevention and control programs which include initiatives targeted toward the indigenous population (PHAC, 2004; PHAC, 2014). However, these programs fail to adequately address the strong association between HIV and TB, and the importance of this association in the treatment and prevention of disease. The need to address this association is further magnified within indigenous populations which suffer from both these infections at aberrantly high rates.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.009 | 0.024 |
| 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".