Evaluation of Integrated-Shared Teaching Within-Class Activity for Basic Science Medical Students
Bibliographic record
Abstract
Background A study at Xavier University School of Medicine introduced an integrated-shared teaching model to address the challenges of medical education. This new approach combines physiology, biochemistry, and pathology into single, organ-system-based sessions, aiming to reduce content redundancy and promote a holistic understanding. This integrated model uses cognitive learning theory to improve retention. By linking subjects like physiology and pathology, it helps students form interconnected mental frameworks called schemata. This reduces cognitive load, moving beyond simple memorization to a deeper, more meaningful understanding that's easier to apply in clinical practice. MethodsThe study included 73 medical students across two cohorts (first-year MD3 and second-year MD5). It used a one-group pretest-posttest design with clinical vignette questionnaires. In addition to the integrated lectures, students participated in interactive in-class assignments, like one-line answers and flowcharts, to encourage real-time engagement and provide formative feedback. ResultsThe results showed significant improvement in learning outcomes for both groups. The first-year MD3 students’ average scores rose from 5.19 to 7.40, while the second-year MD5 students' scores increased from 2.73 to 4.87. Both results were statistically significant (p-value<0.001). In-class activity scores, which averaged 4.45 for MD3 and 3.82 for MD5, confirmed high levels of student engagement and participation. Conclusions In conclusion, this integrated-shared teaching model, when combined with in-class assessments, demonstrably improves knowledge acquisition and retention in basic science medical education. By fostering interdisciplinary connections and reducing cognitive load, this method helps cultivate the critical thinking skills essential for future medical professionals.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".