Availability of health workforce in urban and rural areas in relation to Canadian seniors
Bibliographic record
Abstract
Geographic imbalances in health human resources exist in a health care system when the composition, level, or use of health care providers does not lead to the same optimal health-system goals in all regions. This can lead to inequitable distribution of health care services, particularly for rural and remote populations. This study aims to determine to what extent the distribution of regulated health professionals and seniors in urban and rural areas of the Canadian jurisdictions is different from one another and from the national average. Data used in this study are for the 2016 calendar year. Information about physicians was obtained from the Canadian Institute for Health Information (CIHI) Scott's Medical Database. The data for nurses (nurse practitioners, registered nurses, and licensed practical nurses) were also sourced from CIHI, Health Workforce Database. Geographic information is based on the postal code of physicians' preferred mailing address, and the residence in the case of nurses and the population. Using the Statistical Area Classification from Statistics Canada, each physician and nurse was assigned to either an urban metropolitan, urban non-metropolitan, or rural/remote area. Findings indicate that there were twice as many nurses per 1000 seniors in urban Canada than in rural Canada. However, this gap was threefold in the case of physicians. Provinces with the largest and lowest gap and international comparisons are also provided. Three broad strategies are offered for policymakers in order to mitigate this health workforce imbalance and reduce the regional shortage of nurses and physicians.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".