Can CanMEDS competencies be developed in medical school anatomy laboratories? A literature review
Bibliographic record
Abstract
OBJECTIVES: The purpose of this literature review was to identify potential ways in which undergraduate medical anatomy education may be relevant to the CanMEDS Roles, a competency-based framework used throughout Canadian medical training. METHODS: A scoping review of medical education literature was conducted in March 2017 for English language publications that included key words related to anatomy education and to key competencies formally described for each of the Roles in the CanMEDS 2015 framework. Indicated benefits were then collated, characterized, and synthesized for each CanMEDS Role. RESULTS: There were 71 studies identified describing original findings. Perceived benefits of anatomy education were most often identified for competencies related to the Medical Expert Role. Multiple studies also cited benefits related to the Scholar, Professional and Collaborator Roles. There was a lack of literature related to the Health Advocate, Communicator, and Leader Roles. The majority of benefits defined in the literature were limited to student perceptions rather than objectively measured outcomes. CONCLUSIONS: There is some evidence to suggest that anatomy education can facilitate the development of core competencies related to several CanMEDS Roles, outside of simply developing medical knowledge in the Medical Expert Role. Future studies need to develop methods to objectively assess outcomes related to these competencies.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".