CurrMIT
Bibliographic record
Abstract
The AAMC Curriculum Management & Information Tool (CurrMIT) is a relational database containing curriculum information from medical schools throughout the United States and Canada. CurrMIT can be used to document details of instruction, such as outcome objectives, resources, content, educational methods, assessment methods, and educational sites, which are being employed in curricula. CurrMIT contains basic information about nearly all required courses and clerkships being offered in the United States and Canada. The database contains descriptions of more than 15,000 courses and clerkships; approximately 115,000 "sessions"--e.g., lectures, labs, small-group discussions--and more than 400,000 keywords and word strings documenting the specific details of instruction associated with the courses, clerkships, and sessions. Some specific uses that schools have made of CurrMIT include review of demographics among patient cases being used in a case-based curriculum; comparisons of educational experiences between two geographically separate clinical campuses; and identification of unplanned redundancies and gaps in curricular content. CurrMIT has been designed to accommodate data from virtually any medical school curriculum; "traditional 2+2" curricula, problem-based curricula, and systems-based curricula, and variations of each of these, have been entered in CurrMIT by medical schools. The authors give an overview of the technology upon which the system is built and the training materials and workshops that the AAMC provides to faculty to support CurrMIT's use, and end by describing enhancements being planned for the system.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.511 | 0.394 |
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".