Clinically‐Oriented Verbal Laboratory Assessments in the Gross Anatomy Pre‐Clerkship Medical Curriculum that are Designed to Assess Multiple Competencies
Bibliographic record
Abstract
One of the fundamental principles of a competency‐based medical education curriculum is the integration of the assessment with the curriculum. Assessment needs to be designed to drive learning and similarly learning should drive assessment. Canadian medical schools follow the framework outlined by the Royal College of Physicians and Surgeons of Canada that a competent physician embodies the competencies of all seven CanMeds Roles: Medical Expert, Communicator, Collaborator, Leader, Health Advocate, Scholar, and Professional. All assessments within an integrated competency‐based medical education curriculum should assess more than medical expert. As such, at the Schulich School of Medicine and Dentistry at Western University, we re‐structured traditional bell‐ringer exams into verbal assessments with both individual and group components. Our goal in offering verbal assessments in a group setting was threefold: 1) students would be assessed in three CanMeds Roles: medical expert, collaborator, and communicator; 2) students would be able to receive immediate formative feedback on their knowledge level which in turn would promote mastery of the material; and 3) the verbal assessment would allow us to better assess the application of anatomical knowledge within a clinical context. This talk focusses on the effectiveness and limitations of this new model of assessment. In addition, the process and resources required for the in‐laboratory verbal assessments will be outlined and future directions will be explored. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".