Implementing a Competency-Based Approach to Anatomy Teaching: Beginning With the End in Mind
Bibliographic record
Abstract
BACKGROUND: The shift in the medical education system from a time-based to a competency-based model has encouraged its adoption and application in competency-based education in anatomy classrooms, such as team-based learning models and flipped classroom models. This pilot study aimed to build on previous work of the linkages between anatomy-based learning (a flipped classroom model inspired by a modified team-based learning) and student learning and engagement, and further to assess the linkage between anatomy-based learning and academic performance. METHODS: A sequential mixed-methods design was employed to first gather and analyse quantitative data, including confidential student first semester scores in anatomy: gender, stream, anatomy-based learning, and final anatomy overall mark. The quantitative phase was followed by a qualitative phase in which a series of 8 anatomy laboratories were observed (4 anatomy-based learning and 4 traditional). Thematic analysis was performed on the observation data. RESULTS: Aggregate anatomy-based learning and traditional stream tests, and final unit scores were compared. The anatomy-based learning and final unit scores showed little difference between students in the anatomy-based learning and students in the traditional stream. Students using anatomy-based learning had an aggregate score of 1.15 and final aggregate mark of 72, whereas students in the traditional section had an aggregate score of 1.19 and final mark of 79. Qualitative phase was undertaken to try to assess the linkages between anatomy-based learning and student learning. Observations showed that students in the anatomy-based learning section spent more time on task as compared with their peers in the traditional stream. The anatomy-based learning students also seemed to practice more self-directed learning and employed more multimodal learning strategies than the traditional section stream. DISCUSSION/CONCLUSIONS: Although the quantitative results of this study showed no significant difference in mean scores between anatomy-based learning and traditional designs, it was possible to observe the potential of flipped classroom model in engaging students in individual preparation, in team-based learning, and in consensus-based learning approaches.
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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 | 0.008 | 0.012 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".