Outcomes of 2 Multimodal Human Anatomy Courses Among Doctor of Physical Therapy Students (Entry-Level): A Quasi-experimental Study
Bibliographic record
Abstract
Introduction. Alternative methods of anatomy instruction have increased in popularity; however, cadaveric dissections were not consistently reported as the most effective teaching tool. Subjects from 2 professional (entry-level) physical therapist education programs who were taught anatomy using multimodal strategies and either cadaveric dissection or prosected cadavers were compared. The purposes of this study were to 1) determine subjects' approach to learning (surface or deep), 2) determine the preferred learning style of the subjects, 3) assess the subjects' retention of anatomy at the completion of an anatomy course and 6 months later, and 4) determine how much time subjects spent in learning activities for each anatomy pedagogy. Methods. Outcome measures consisted of an anatomy quiz, the Revised 2-Factor Study Process Questionnaire, a Learning Perception Inventory, and the Visual, Auditory, Read/Write, Kinesthetic Questionnaire. Data were collected at 3 points during the study: before anatomy class, immediately at the conclusion of the anatomy class, and 6 months after the class had ended. Data were analyzed using SPSS 25.0 and included descriptive statistics, Wilcoxon signed ranks tests, and Mann–Whitney U tests. Results. Subjects in both programs were kinesthetic learners who used a deep learning approach. Subjects were able to retain anatomical knowledge postanatomy and 6 months after the class ended, no matter which learning tools were used. The group who worked with prosected cadavers perceived spending more total time preparing for anatomy class. Discussion and Conclusion. Based on these results, cadaveric prosection was as effective as cadaveric dissection in 2 multimodal anatomy classes for subjects in 2 professional (entry-level) physical therapist education programs.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".