It's about TIME: a general‐purpose taxonomy of subjects in medical education
Bibliographic record
Abstract
CONTEXT: Modern computer technology permits the creation of detailed, dynamic electronic curriculum maps to facilitate curriculum searching, organisation and quality assurance. However, when attempting to map curricular content, a common question to arise is: 'To what should we map our curriculum?' With respect to content (i.e. the subject being taught, learned or examined), mapping to terminal outcomes or competencies may be too broad, whereas mapping to learning objectives is too specific. METHODS: To address this problem, the authors created TIME-ITEM (topics for indexing medical education; en Français: index des thèmes pour l'éducation médicale), a hierarchical taxonomy of topics relevant to medical education. It is a general-purpose, intermediate-granularity, standardised index that covers the entire range of subject matter in medical education. The content and structure of topics within TIME was developed in consultation with medical educators and librarians at several Canadian medical schools. As far as possible, the language used is standardised to the Unified Medical Language System. RESULTS: TIME is available as a web application that allows users from various schools to enter their school-specific outcomes, competencies and learning objectives, and then link these to the standardised topics in a way that is meaningful to the school. The entire TIME content and structure can then be exported, via xml, to external applications and used as an index for curriculum mapping, meta-tagging learning objects, or categorising examination questions. TIME can be viewed at http://www.time-item.org (username: 'guest'; password: 'guest').
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".