Is Integration Within Anatomical Sciences Important? Assessing Medical Student Learning of Histology and Embryology via Interdisciplinary Causal Mechanisms
Bibliographic record
Abstract
Curriculum integration is increasingly recognized as a critical component of undergraduate medical education. One approach to curricular integration is through the use of causal mechanisms, which are links that guide students in making purposeful connections between two distinct disciplines. Woods et al. have shown that integrating basic science with clinical content in various contexts using causal mechanisms results in improved diagnostic performance in learners. It remains unknown whether utilizing a similar integrated approach during instruction of different basic sciences, such as embryology and histology, results in better understanding and application of those disciplines. The aim of this study was to determine the effects of integrating histology and embryology using causal mechanisms on medical students’ immediate and delayed (1‐week) recall and application. Second‐year medical students at Oakland University William Beaumont School of Medicine (n=52) participated in this study and were randomized to either an experimental (n=27) or control group (n=25). The experimental group watched a video about pituitary histology and embryology that explicitly integrated these disciplines through causal mechanisms. The control group watched a video covering the same content, but lacked interdisciplinary causal mechanisms. Counterbalanced immediate and delayed post‐tests assessed recall and application of the content (Blooming Anatomy Tool level 1 and 3 questions, respectively). No significant differences (2‐way ANOVA with Bonferroni correction) were observed between the groups on immediate or delayed tests in terms of overall score (p=0.48), histology subscore (p=0.42), embryology subscore (p=0.78), recall subscore (p=0.64), and application subscore (p=0.61). The lack of significant differences between the two learning groups may be due to the close proximity (temporal integration) of embryology and histology instruction afforded to all participants, despite the control group not receiving instruction with causal mechanisms. It is possible that the explicit integration of causal mechanisms does not provide added value when simultaneously learning embryology and histology. Further research is warranted to identify the effect of temporal integration on medical students’ learning of distinct disciplines within the anatomical sciences. Support or Funding Information OUWB Fellowship in Medical Education
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".