The economic contribution of the Northern Ontario School of Medicine to communities participating in distributed medical education.
Bibliographic record
Abstract
INTRODUCTION: The economic contribution of medical schools to major urban centres can be substantial, but there is little information on the contribution to the economy of participating communities made by schools that provide education and training away from major cities and academic health science centres. We sought to assess the economic contribution of the Northern Ontario School of Medicine (NOSM) to northern Ontario communities participating in NOSM's distributed medical education programs. METHODS: We developed a local economic model and used actual expenditures from 2007/08 to assess the economic contribution of NOSM to communities in northern Ontario. We also estimated the economic contribution of medical students or residents participating in different programs in communities away from the university campuses. To explore broader economic effects, we conducted semistructured interviews with leaders in education, health care and politics in northern Ontario. RESULTS: The total economic contribution to northern Ontario was $67.1 million based on $36.3 million in spending by NOSM and $1.0 million spent by students. Economic contributions were greatest in the university campus cities of Thunder Bay ($26.7 million) and Sudbury ($30.4 million), and $0.8-$1.2 million accrued to the next 3 largest population centres. Communities might realize an economic contribution of $7300-$103 900 per pair of medical learners per placement. Several of the 59 interviewees remarked that the dollar amount could be small to moderate but had broader economic implications. CONCLUSION: Distributed medical education at the NOSM resulted in a substantial economic contribution to participating communities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".