Are We Effectively Teaching the Basic Sciences? The Influence of Pedagogical Methods on 3rd Year Medical Students' Basic Science Knowledge Retention in an Undergraduate Medical Education Curriculum
Bibliographic record
Abstract
Introduction Basic sciences (anatomy, biochem., microbio., path., pharm., phys.) are essential in undergraduate medical education (UME), as they provide a framework on which clinical concepts can be built. However, studies demonstrate that students' basic science knowledge retention can drop to as low as 46% within 10 months. Additionally, the pedagogical methods used for teaching the basic sciences varies widely among institutions, and research indicates that knowledge retention varies with pedagogical methods. Active learning is typically related to higher retention rates compared to didactic lectures; however, some studies have shown that this is not always the case. Currently, it is unclear if students learn and retain the most relevant basic science concepts from pre‐clerkship, and if current UME curricula are employing the most effective pedagogical methods to teach the basic sciences. Thus, this study aims to: 1) assess clerks' basic science knowledge level prior to and at the completion of each clerkship rotation, and 2) evaluate the effectiveness of the current pedagogical methods employed in Schulich's basic science pre‐clerkship curriculum. Methods 1) A basic science assessment comprised of clinical vignette MCQ's was created for each clerkship rotation (family med., psych., paeds., OBGYN, surgery, internal med.). Assessments were distributed as a pre‐ and post‐test for each rotation to assess clerks' knowledge retention and learning. 2) The assessed basic science concepts were then mapped and correlated to the pedagogical method in which the concept was taught. Results 1) Assessment data revealed that clerks achieved an average passing grade (≥60%) on each pre‐test. There was a significant increase between pre‐ and post‐test scores for all rotations (p≤0.05); however, the greatest improvement (10%) was identified for clerks completing paeds (p=0.001). 2) Mapping data elucidated that the majority of the assessed basic science concepts are currently being taught via didactic lectures (60–83%), while 11–27% are taught using active learning methods. There was a strong correlation between the percent of didactic lectures and student assessment scores (R 2 =.890, p=.005), while active learning methods only had a moderate correlation (R 2 =.668, p=.047). Conclusions Results indicate that clerks have an adequate knowledge of basic science concepts prior to each rotation, and further learned concepts during clerkship. The large increase identified in paeds. may reflect the high volume of in‐class instruction that occurs during that rotation. Additionally, although Schulich UME currently employs both active and didactic methods, data indicate that students achieved the highest retention scores on basic science concepts taught primarily via didactic lectures. This may be due to the fact that students are familiar with learning the basic sciences didactically, and the associated MCQ testing style. Thus, our data suggest that didactic lectures are an effective way to promote knowledge acquisition and retention in Schulich's basic science pre‐clerkship curriculum. Support or Funding Information AAA, OGS This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".