Organizational Models of Educational Technology in U.S. and Canadian Medical Schools
Bibliographic record
Abstract
PURPOSE: To examine the organizational structure of educational technology units within U.S. and Canadian medical schools in order to (1) identify organization models that support educational technology, (2) describe key attributes of these models, and (3) discuss the strengths and challenges associated with these models. METHOD: The authors distributed a survey to 88 schools that had previously provided information on their educational technology services and infrastructure. The authors developed the survey through a series of pilots and, then, from the data for each respondent school, created concept maps, which were used to identify organizational models. The authors conducted analyses to determine differences among models. The authors coded the comments about organizational models and identified themes. RESULTS: The authors received adequate data for analysis from 61 schools (69%). Four models for educational technology organizations emerged: (1) centralized units located in the school of medicine, (2) centralized units located at the health science center, (3) centralized units at the comprehensive university, and (4) no centralized unit (Dispersed Model). The majority (75%) of schools relied on some type of centralized organization. Whereas few organization attributes proved to be statistically significant, the centralized models have more resources devoted to educational technology and a closer alignment with the academic mission than the Dispersed Model. CONCLUSIONS: Medical schools primarily use central models. The authors recommend that schools structuring their educational technology resources consider exploration of a central model because of its focused use of resources to improve teaching and learning.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".