From Innovation to Intrapreneurship: Fostering academic success via the GridlockED project and innovation fund
Bibliographic record
Abstract
Background: Funding for educational innovations is increasingly scarce in academic medicine. While there is some funding for medical education research, this is often for discovery or application work, and there are few avenues for those with a heavy innovation focus to fund early work. Objective of the innovation: The objective was to develop an intrapreneurial unit focused on medical education projects and scholarship. Development process and implementation: The GridlockED and TriagED games are educational or serious games that seek to teach health care learners about emergency medicine processes. Both games were cocreated with learners and brought to market in the past 3 years. All of the proceeds from the sales of these games have been accrued over time to create a new innovation fund. This fund seeks to support trainees and early career educators in their medical education projects. Outcomes: Sales for GridlockED began in March 2018 and the TriagED began in November 2019. In the first year, sales for GridlockED yielded a total of $9,534. After 18 months of sales, the fund has accrued a total of $14,530. The fund has helped finance the development of new games. Additionally, the fund awarded two internal $500 Kickstarter grants to assist with evaluating and improving two local education projects. The GridlockED and TriagED games have also spurred multiple academic opportunities for junior educators interested in this domain: five workshops, eight conference abstracts, two peer-reviewed papers, and two research protocols are being developed. Conclusions: The GridlockED and TriagED games represent a new academically oriented, intrapreneurial approach to medical education work.
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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 | 0.021 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.003 | 0.046 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.014 |
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".