Care interrupted: Poverty, in-migration, and primary care in rural resource towns
Bibliographic record
Abstract
Internationally, rural people have poorer health outcomes relative to their urban counterparts, and primary care providers face particular challenges in rural and remote regions. Drawing on ethnographic fieldnotes and 14 open-ended qualitative interviews with care providers and chronic pain patients in two remote resource communities in Northern Ontario, Canada, this article examines the challenges involved in providing and receiving primary care for complex chronic conditions in these communities. Both towns struggle with high unemployment in the aftermath of industry closure, and are characterized by an abundance of affordable housing. Many of the challenges that care providers face and that patients experience are well-documented in Canadian and international literature on rural and remote health, and health care in resource towns (e.g. lack of specialized care, difficulty with recruitment and retention of care providers, heavy workload for existing care providers). However, our study also documents the recent in-migration of low-income, largely working-age people with complex chronic conditions who are drawn to the region by the low cost of housing. We discuss the ways in which the needs of these in-migrants compound existing challenges to rural primary care provision. To our knowledge, our study is the first to document both this migration trend, and the implications of this for primary care. In the interest of patient health and care provider well-being, existing health and social services will likely need to be expanded to meet the needs of these in-migrants.
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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 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".