© 2005 CMA Media Inc. or its licensors
Bibliographic record
Abstract
age of physicians in rural areas, particularly in the northern parts of the province and on the remoter parts of Vancouver Island. People living in these areas have reduced access to health care and poorer health outcomes compared with people in the rest of BC.1 In Canada, more than 9 million people, 30.4 % of the population, live in predominantly rural regions. Al-though 20.6 % of Canadian resi-dents live in towns of under 10 000 people, they are served by only 9.3 % of the country’s physicians. The province has 1 accred-ited medical school, at the Uni-versity of British Columbia (UBC). In 2003 its intake of stu-dents was 128, despite a pro-jected annual need in BC of at least 300 physicians.2 This situation led in 2000 to the development of an academ-ic, community and political will to address the growing physi-cian shortage quickly, by means that would fit the geography and culture of BC. Citizen protests in rural areas and a strike in 2000 by physicians in the northern city of Prince George further focused atten-tion on the distribution of health resources. There are 2 broad education-al approaches to increasing the number of physicians per capita and correcting the rural–urban maldistribution: the distributed approach, in which the medical school that is already established develops branch operations in remote areas, and new start, which involves building an addi-tional medical school in a re-mote area. Both have been tried. There is growing evidence that students from smaller com-munities are more likely to end up working in rural areas than those from larger ones, and that physicians are more likely to work near where they trained.3 On that basis, UBC created its own solution to BC’s shortage of northern and rural physicians by developing 3 geographically distributed campuses in partner-ship with 2 existing universities:
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.911 | 0.911 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".