The geographic pipeline to Rural Family Medicine at Memorial University
Bibliographic record
Abstract
Introducao: The Canadian province of Newfoundland and Labrador has a significant rural population, with 52% of the population living in communities of under 10,000 people and an additional 10% in communities of between 10,000 and 25,000 people. Producing family doctors to work in these communities is an important task of the medical school at Memorial University of Newfoundland. Objetivos: Memorial University’s “pipeline” approach to producing rural family doctors involves recruiting rural students and providing rural placements. This study measures and reports on Memorial’s success in both areas. Metodologia ou Descricao da Experiencia: This study uses administrative data for MUNMED graduating classes 2011 and 2012 to describe backgrounds and educational placements of these students. This study also reports on practice locations of all MUNMED graduates practicing family medicine in Newfoundland and Labrador. StatsCan population data was used to classify locations as follows: small rural community (<10,000 population); small rural city (10,000-24,999 population); medium city (24,999-99,999 population), large city (100,00-499,999 population), very large city (500,000-999,999 population) and metropolis (over 1,000,000 population). SPSS was used to calculate frequencies and ArcGIS was used to map results. Resultados: Of 120 students who graduated in 2011 and 2012, 32% had rural backgrounds. For graduating classes 2011-2012, 65% of Year 1 Community Health placement weeks took place in small rural communities and 19% took place in small rural cities; 41% of Year 2 FM Community placement weeks took place in small rural communities and 14% took place in small rural cities; 88% of Year 3 FM placement weeks took place in small rural communities and 6% took place in small rural cities. Of 297 MUNMED graduates currently practicing family medicine in Newfoundland and Labrador, 64 (22%) are practicing in small rural cities and 44 (15%) are practicing in small rural communities of under 10,000. Conclusao ou Hipoteses: External data confirms our conclusion that Memorial’s pipeline to rural family practice is successful: In 2010, Memorial received the Keith award for having the highest percentage of FM graduates (52%) working in rural practice 10 years after graduation. In 2013, Memorial received the Keith award again, having 44% of FM graduates in rural practice 10 years after graduation.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.001 |
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".