Community Setting as a Determinant of Health for Indigenous Peoples Living in the Prairie Provinces of Canada: High Rates and Advanced Presentations of Tuberculosis
Bibliographic record
Abstract
Indigenous Peoples in Canada experience disproportionately high tuberculosis (TB) rates, and those living in the Prairie Provinces have the most advanced TB presentations (Health Canada, 2009). The community settings (i.e., urban centres, non-remote reserves, remote reserves, and isolated reserves) where Indigenous Peoples live can help explain high TB rates. Through qualitative description, we identify how community setting influenced Indigenous people’s experiences by (a) delaying accurate diagnoses; (b) perpetuating shame and stigma; and (c) limiting understanding of the disease. Participants living in urban centres experienced significant difficulties obtaining an accurate diagnosis. Reserve community participants feared being shamed and stigmatized. TB information had little impact on participants’ TB knowledge, regardless of where they lived. Multiple misdiagnoses (primarily among urban centre participants), being shamed for having the disease (primarily reserve community participants), and a lack of understanding of TB can all contribute to advanced presentations and high rates of the disease among Indigenous Peoples of the Prairie Provinces.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".