Effects of four supplemental instruction programs on students' learning of gross anatomy
Bibliographic record
Abstract
Many researchers have reported that supplemental instruction programs improve medical students' performance in various basic sciences. This study was conducted to evaluate the summative effects of four supplemental instruction programs (i.e., second-year medical student teaching assistant program; directed study program; weekly instructor laboratory reviews; and a web-based anatomy program) on medical students' gross anatomy laboratory practical performance. First-year medical students from the graduating class of 2006 (n = 223) received the four supplemental instruction programs (Experimental Group). The Control Group consisted of first-year medical students from the graduating class of 2005 (n = 254) who did not receive the four supplemental learning methods. Mann-Whitney rank sum tests were used to compare the two groups' median percentages for the back-upper limb (B-UL) and the lower limb (LL) parts of a gross anatomy laboratory practical. The Experimental Group's median percentages for both the B-UL (77.78%) and LL (83.33%) were significantly greater than that of the Control Group (B-UL = 69.00%; LL = 81.00%; P < 0.05). Results from a post-hoc student survey showed that more students both rated and ranked the weekly instructor laboratory reviews as extremely useful and most beneficial, respectively. A greater number of students rated and ranked the web-based anatomy program as not useful and least beneficial, respectively. The results from this study suggest that the four supplemental instruction programs improved students' learning of gross anatomy as measured by laboratory practical performance. In addition, students most valued the additional time in the gross anatomy laboratory with the instructors.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".