CANmeds physician competencies inadvertently realized in the gross anatomy lab (535.4)
Bibliographic record
Abstract
Introduction: The cadaveric gross anatomy lab is a learning environment in which the students learn multiple skills and tasks. In the process of learning the intricacies of the human body, the students learn, demonstrate and acquire competencies such as professionalism, collaboration, communication and scholarly learning. This study was based on student evaluations to measure the degree to which the listed competencies are realised in the Gross Anatomy Lab. Observations:The first lab session starts with a memorial service to honour and respect the generous “gift” by the donors. This sets the tone for gratitude, respect and responsibility with which the learners treat their ‘first patient’ whenever they are in the lab. They approach the learning as a group and share the information and participate in the discussions among themselves as well as the rest of the class when there is an unusual finding. The learning environment calls for collaboration and communication within the specific group and also with the rest of the class. Learners’ feedback: At the end of each course the evaluations polled on the professionalism, collaboration and scholarly learning were rated at 4.3/5 and 4.4/5 among year one and two respectively. Conclusion: The learning environment in the gross anatomy lab can effectively nurture competencies such as professionalism, collaboration, communication and scholarly learning in addition to medical expert. The anonymous evaluation of the learning environment, by the learners, also provided some comments regarding the strong collaborative environment and team work. The scores as well as the comments are strong indicators that the learners are realizing other competencies such as professionalism, communication and collaboration to the same level as that of medical expert. Medical schools therefore should be aware of the competencies realized in the gross anatomy lab during the process curriculum design.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.122 | 0.023 |
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".