Bibliographic record
Abstract
Introducao: In Canada, the mortality rate increases with distance from an urban center. The rural-urban divide is highest amongst the younger population under 45 years of age. In 2011, 18 percent of Canadians live outside the metropolitan area and 15 percent of family physicians live in rural communities. Memorial Medical School has a social accountability mandate and focuses the training of rural physicians. Objetivos: The objective of this presentation is to describe the Northern Family Medicine (Norfam) program of Memorial University and the impact this program has on physician human resources and services. Metodologia ou Descricao da Experiencia: The Norfam program is based in remote community of 7,600 with a catchment population of 15,000 dispersed over an area the size of England. Norfam is involved in the whole pipeline from high school, premed institute, all the 4 years of undergraduate medical school, family medicine and pediatric residencies, PhD training and professional development. Resultados: Graduates of Norfam allowed us to fill all the physician positions. 90% of Norfam graduates worked in rural Canada. More local students are applying to medical school. The entire 5 Indigenous students from the 1st batch of Premed have entered a health professional training (with 3 in medicine). Three students from the 2nd batch are currently completing their current undergraduate studies and 2 have applied for medical school. Norfam is associated with a drop in infant mortality rate from 3 times the Canadian average to at par. Applied research have addressed previously high prevalence of occupational crab asthma and Hepatitis B. We are working our priorities, suicide and diabetes mellitus. Conclusao ou Hipoteses: Training physicians in rural settings helps address physician human resource issue and address the disparities in health status. It allows our medical school to be socially accountable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".