Step-by-Step Process for Assessing the Economic Impact of Regional Medical Campuses in Canada
Bibliographic record
Abstract
Background: Regional medical campuses (RMCs) create positive economic impacts in communities and small cities. RMCs increase educational capacity, medical services, and address the shortage or maldistribution of physicians in rural areas. Our paper answers the question: How do you assess the economic impact of a RMC? Methods: The Canadian Input-Output (I-O) model and the Simplified American Council on Education (ACE) model are adapted to assess the economic impact of an individual RMC using a step-by-step process. The models are tested using data from three Canadian RMCs. Results: A comparison of the two models found similarities with data requirements and spreadsheet calculations. However, the Canadian I-O model spreadsheet is linked to Statistics Canada multipliers and calculations are more complex. Outputs are calculated for multiple economic variables. The Simplified ACE model, in contrast, uses a single multiplier and provides a single number by input category and a cumulative total of all impacts. Conclusion: Both models successfully assess economic impacts of RMC. The step-by-step process allows RMC administrators and others to understand the limitations of each model, but also facilitates an in-house economic assessment of RMC. The authors provide guidance on choosing the best model.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; a candidate call from one teacher head, 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".