Competencies 101: The knowledge, skills, behaviors and attitudes of a top notch anatomy educator
Bibliographic record
Abstract
The Liaison Committee on Medical Education (LCME) defines the standards of accreditation for medical schools in the United States and Canada. These standards are stated in an annual publication titled “Functions and Structure of a Medical School”, however recently this publication has undergone significant revision. In the current version of this document published in March 2014, the former 132 standards have been distilled down to 12. It is important for educators to become familiar with the new standards, as they govern the design and implementation of the components of the curriculum. Standard 6 discusses competencies, curricular objectives and curricular design. Anatomists can assist their institution in meeting this standard by: 1) writing learning objectives in outcome‐based terms and distributing them to all students and faculty; 2) creating self‐directed learning experiences in their courses; 3) developing electives in the anatomical sciences that allow students to explore their areas of interest in depth; 4) providing opportunities for anatomy‐based community service activities; and 5) developing interprofessional activities in the anatomical sciences.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".