The four-legged kitchen stool. Recruitment and retention of rural family physicians.
Bibliographic record
Abstract
Canada is the second-largest country in the world, covering 10 million square kilometres. Our population density is sparse by international standards. Canada is one of the most urbanized nations in the world, however, with one third of the population living in Montreal, Toronto, and Vancouver and 90% living within several hundred kilometres of the American border.1 Approximately 20% of Canadians live in communities of less than 10 000 people. Providing equitable and sustainable health care services to rural Canada is challenging; our extreme weather conditions and diverse geography prove obstacles to traveling over the vast distances and difficult terrain between our scattered communities. For small remote communities, it is hard to provide required technology and adequate numbers of health professionals. This dilemma is shared by all regions in Canada and by most countries around the world. Rural communities suffer from a chronic shortage of family physicians who are challenged to provide primary and secondary medical care to patients who are older, poorer, less healthy, less well educated, and more likely to be obese and to smoke than Canadian patients in urban areas.2-4
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.021 | 0.016 |
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".