Sharing innovation: the case for technology standards in health professions education
Bibliographic record
Abstract
Information technologies have provided fertile ground for innovation in healthcare education, but too often these innovations have been limited in scope and impact. One way of addressing these limitations is the development of common and open technology standards to scale innovation across organizational boundaries. Research on the diffusion of standards indicates that environmental forces, such as regulatory changes, top-down management support, and feasibility are key determinants of standards adoption. This paper describes the perspective and work of MedBiquitous, the only internationally recognized standards body in healthcare education. Many innovators are implementing MedBiquitous healthcare education standards to effect change within and across organizations. In a resource-constrained and knowledge intensive domain such as healthcare education, collaboration is an imperative. Technology standards are essential to raise the quality of healthcare education and assessment in a cost-effective manner.
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 | 0.072 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.022 | 0.099 |
| Scholarly communication | 0.031 | 0.052 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.030 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".