Knowledge loss of medical students on first year basic science courses at the university of Saskatchewan
Bibliographic record
Abstract
BACKGROUND: Many senior undergraduate students from the University of Saskatchewan indicated informally that they did not remember much from their first year courses and wondered why we were teaching content that did not seem relevant to later clinical work or studies. To determine the extent of the problem a course evaluation study that measured the knowledge loss of medical students on selected first year courses was conducted. This study replicates previous memory decrement studies with three first year medicine basic science courses, something that was not found in the literature. It was expected that some courses would show more and some courses would show less knowledge loss. METHODS: In the spring of 2004 over 20 students were recruited to retake questions from three first year courses: Immunology, physiology, and neuroanatomy. Student scores on the selected questions at the time of the final examination in May 2003 (the 'test') were compared with their scores on the questions 10 or 11 months later (the 're-test') using paired samples t -tests. A repeated-measures MANOVA was used to compare the test and re-test scores among the three courses. The re-test scores were matched with the overall student ratings of the courses and the student scores on the May 2003 examinations. RESULTS: A statistically significant main effect of knowledge loss (F = 297.385; p < .001) and an interaction effect by course (F = 46.081; p < .001) were found. The students' scores in the Immunology course dropped 13.1%, 46.5% in Neuroanatomy, and 16.1% in physiology. Bonferroni post hoc comparisons showed a significant difference between Neuroanatomy and Physiology (mean difference of 10.7, p = .004). CONCLUSION: There was considerable knowledge loss among medical students in the three basic science courses tested and this loss was not uniform across courses. Knowledge loss does not seem to be related to the marks on the final examination or the assessment of course quality by the students.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".