Patterns of Health Services Utilization in Rural Canada
Bibliographic record
Abstract
CONTEXT OF THIS STUDY: Canadians value ease of access to their health services. Although many studies have focused on accessibility to health services in Canada, few have examined rural-urban differences in this aspect, particularly from a national perspective. Yet disparities in access to health services exist between rural and urban populations, as do the challenges of delivering health care to more remote areas or to those with small populations. "Canada's Rural Communities: Understanding Rural Health and Its Determinants" is a three-year research project co-funded by the Canadian Population Health Initiative (CPHI) of the Canadian Institute for Health Information (CIHI) and the Public Health Agency of Canada (PHAC). It involves investigators from the Public Health Agency of Canada, the Centre for Rural and Northern Health Research (CRaNHR) at Laurentian University, and other researchers. The first publication of the research project was How Healthy Are Rural Canadians? An Assessment of Their Health Status and Health Determinants; this, the second publication, is a descriptive analysis of the utilization patterns of a broad range of health services by rural residents compared to their urban counterparts.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".