A review of methods to assess the economic impact of distributed medical education (DME) in Canada
Bibliographic record
Abstract
BACKGROUND: Canadian distributed medical education (DME) increased substantially in the last decade, resulting in positive economic impacts to local communities. A reliable and simple method to estimate economic contributions is essential to provide managers with information on the extent of these impacts. This review paper fills a gap in the literature by answering the question: What are the most applicable quantitative methods to assess the economic impact of Canadian DME programs? METHODS: The literature is reviewed to identify economic assessment methods. These are evaluated and compared based on the benefits, challenges, data needs, outputs and potential for use in the DME context. RESULTS: We identified five economic impact methods used in similar contexts. Two of these methods have the potential for Canadian DME programs: the Canadian Input-Output (I-O) model and the Simplified American Council on Education (ACE) method. CONCLUSION: Choice of a method is contingent on the ability to measure the salient economic impacts, and provide an output that facilitates sustainable decision making. This paper thus fills a gap by identifying methods applicable to DME. These methods will assist stakeholders to calculate economic impacts, resulting in both the advancement and sustainability of these programs over short-and long-term time frames.
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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 | 0.018 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".