Neuroanatomy Education: Evaluating Methods Of Delivery On Student Opinions And Academic Performance
Bibliographic record
Abstract
INTRODUCTION Medical schools have traditionally used a dissection‐based approach for educating students in neuroanatomy. There is a trend towards increased use of prosected specimens, models, and 3‐D imaging materials as learning resources. It is not known which method constitutes the most effective way to teach students a basic understanding of human neuroanatomy. The purpose of this study was to examine whether the method of educational delivery influences student perceptions about learning anatomy, as well as performance on a practical exam. METHODS Undergraduate students from the same medical school were compared. One cohort was taught using a method that involved some dissection of brain specimens while the the second cohort was taught using a method of delivery that employed more prosected specimens. Contact hours for both groups were similar. A standardized educational survey was used to collect information regarding student perceptions and evaluate 6 different methods of delivery. This data was compared against student performance on neuroanatomy practical exams. SUMMARY Seventy students (dissection=40, prosection=30) completed the survey. Survey results revealed that teaching that used clinical cases was the most effective approach for relating anatomical structure to function, while the use of medical imaging was the most effective approach for instilling anatomical knowledge. When comparing the performance of students on the lab practical exam, the dissection group outperformed the prosection group (mean=83% vs 72%). The dissection group also outperformed the prosection group on exam questions that were given to both groups (mean=80% vs 76%). CONCLUSIONS Data suggest that student perceptions and academic performance are influenced by the method of delivery, and that medical imaging and case based scenarios enhance the learning environment. The results will be used to guide the selection of delivery method in future neuroanatomy curriculum. Support or Funding Information Funding provided by the Teaching and Learning Enhancement Fund of the University of Manitoba.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".