Cost-Efficient Medical Education: An Innovative Approach to Creating Educational Products
Bibliographic record
Abstract
BACKGROUND: Cost is a barrier to creating educational resources, and new educational initiatives are often limited in distribution. Medical training programs must develop strategies to create and implement cost-effective educational programming. OBJECTIVE: We developed high-quality medical programming in procedural instruction with efficient economics, reaching the most trainees at the lowest cost. METHODS: The Just-In-Time online procedural program was developed at the University of Toronto in Canada, aiming to teach thoracentesis, paracentesis, and lumbar puncture skills to internal medicine trainees. Commercial vendors quoted between CAD $50,000 and $100,000 to create 3 comprehensive e-learning procedural modules-a cost that was prohibitive. Modules were therefore developed internally, utilizing 4 principles aimed at decreasing costs while creating efficiencies: targeting talent, finding value abroad, open source expansion, and extrapolating efficiency. RESULTS: Procedural modules for thoracentesis, paracentesis, and lumbar puncture were created for a total cost of CAD $1,200, less than 3% of the anticipated cost in utilizing traditional commercial vendors. From November 2016 until October 2018, 1800 online instructional sessions have occurred, with over 3600 pageviews of content utilized. While half of the instructional sessions occurred within the city of Toronto, utilization was documented in 10 other cities across Canada. CONCLUSIONS: The Just-in-Time online instructional program successfully created 3 procedural modules at a fraction of the anticipated cost and appeared acceptable to residents based on website utilization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".