Integrating the educational technology expert in medical education: A role-based competency framework
Bibliographic record
Abstract
This article was migrated. The article was not marked as recommended. Even though educational technology has existed for decades, integrating educational technology into the medical curriculum has just recently come to the forefront as a priority for the Royal College of Physician and Surgeons of Canada. The process for how this integration will occur has yet to be defined. Therefore, a competency profile was developed for the educational technologist, comprising seven roles, based on the authors' and collaborators' professional knowledge and experience, along with a scoping review of the literature. The result is a hybrid framework of seven core roles constellated around a central role of educational technologist, similar to the CanMEDS model. The proposed roles are: Educational Technology Expert, Leader, Educator, Administrator, Developer, Designer, and Collaborator. Each role has a definition, list of competencies and example activities. A description of each role is provided, along with key concepts highlighted. This newly proposed roles' framework is readily identifiable to the medical educator familiar with CanMEDS, and is presented to facilitate integration between medical educators and educational technologists. The model presents a familiar humanist lens through which to view educational technology. Using the MedEdPublish platform for dissemination of this work, ongoing dialogue regarding the proposed framework, particularly regarding its roles, content, and applicability, is greatly encouraged in the reviews' section.
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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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".