Geography as a Determinant of HIV Health Outcomes for Individuals Living in Rural, Remote, and Northern Canada
Bibliographic record
Abstract
Individuals in rural and remote areas face significant barriers to chronic disease care, including HIV. Challenges in defining and conceptualizing rurality and the lack of appropriate community engagement are ongoing limitations of rural health research. The objectives of this thesis are to: reflect on the importance of developing meaningful community relationships when engaging in rural health research impacting Indigenous peoples, engage with Indigenous community members to explore how distance to HIV care impacts quality of HIV care and, explore how different definitions of rural and northern impact the health outcome, HIV virologic suppression. This thesis explores my relationships as a settler physician engaging with Indigenous communities through a Two-Eyed Seeing approach. It focuses on co-writing methodologies to include perspectives from Indigenous and settler team members. Cohort data from the Canadian Observational HIV Cohort were used to explore the impact of distance to HIV care on markers of quality HIV care in Saskatchewan, as measured by the Positive Partnership Score. The Positive Partnership Score is a composite metric measuring quality of HIV care and includes frequency of viral load and CD4 measurements, baseline CD4 count, antiretroviral medication regimen, and virologic suppression. Cohort data from the British Columbia Comparative Outcomes And Service Utilization Trends cohort were used to explore the impact of different definitions of rurality on HIV virologic suppression. In Saskatchewan, living further from HIV specialist care or from a site of research enrollment was associated with lower Positive Partnership Scores. In British Columbia, rurality defined by Statistical Area Classification but not by Forward Sortation Address was associated with lower odds of virologic suppression. Northern Health Authority was associated with the lowest odds of virologic suppression. Rural health discrepancies have been frequently reported across many health conditions. Greater efforts are needed to address geographic health disparities in Canada. More significantly, this thesis demonstrates several lessons for how rural research should be conducted: careful consideration is needed to ensure geographic variable selection is conceptually appropriate for the research question and community members, such as Indigenous people living with HIV can enrich rural health research and should be included as team members.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".