Impact of Inter-Community and Inter-Jurisdictional Mobility of First Nations on Tuberculosis and Tuberculosis Prevention and Care Programming
Bibliographic record
Abstract
Tuberculosis (TB) rates are disproportionately higher among Indigenous people than Canadian-born, non-Indigenous populations, including in Saskatchewan. Among other factors, inter-jurisdictional mobility of First Nations people between Alberta (AB) and Saskatchewan (SK) may contribute to persistence of TB by disrupting prevention and care programming. This research explores the potential impact of inter-jurisdictional mobility of First Nations on existing tuberculosis prevention and care programs. Objectives include: (1) identify mobility patterns, (2) assess current policies covering First Nations TB prevention and care programming, and (3) evaluate the potential impact of inter-jurisdictional mobility on TB and TB programming. The study community is a remote First Nation community in northern Saskatchewan located near the Alberta border. I conducted semi-structured interviews with community participants around mobility patterns. A multi-level document review of TB prevention and care policies and interviews with healthcare providers in both provinces were also conducted. Due to the rural location of the community, external mobility is frequent. Motivations include healthcare, work, family, entertainment, shopping, traditions, and others. Frequency and destinations of travel vary by season with inter-provincial mobility to Alberta being most common in the winter via temporary winter roads. Currently, there are no federal or provincial policies or procedures in place for mobile TB patients. However, health care workers follow a standard treatment procedure for mobile patients. Lines of communication between provinces and communities are clear but direct communication between communities is not, currently there is no prescriptive path of communication between jurisdictions. Without established policies, the possibility of treatment non-completion and failure may be increased, as patients may "slip through the cracks.” Specific policies across jurisdictions are needed to address this. Clear policies and communication paths, and inter-jurisdictional coordination, can increase seamless care for mobile patients. This study supports the growing literature on TB among Indigenous populations and contributes to addressing mobility as a determinant of health.
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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 | 0.002 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".