Evaluating student understanding of core pharmacokinetic concepts
Bibliographic record
Abstract
Both educators and graduates have expressed concern about a perceived pharmacology knowledge gap that includes difficulty applying fundamental principles to clinical and research problems. Consequently, we sought to determine the extent to which current students can explain the meaning of, and appropriately apply, a subset of core concepts, and to identify any misconceptions arising from the responses. Of the twenty-four pharmacology core concepts arising from the recent international collaboration, four pharmacokinetic concepts were chosen, namely drug bioavailability, drug clearance, volume of distribution, and steady-state concentration. A total of 318 students from 11 universities across seven countries chose to participate in this study. Expert analysts identified the essential elements for each concept, then independently assessed each student's response. Teams of two experts compared their evaluations to reach a consensus and grouped misconceptions thematically. For each core concept, less than 30% of students provided responses that encompassed all essential elements. Participants found drug clearance most challenging, generally conflating it with the rate of elimination, whereas they demonstrated a better understanding of drug bioavailability. There were 34 misconception themes coded in a total of 813 statements, with volume of distribution and drug clearance producing the highest numbers (13 and 12, respectively). Overall, results suggest that students found it easier to apply the concept than to explain its meaning, which might reflect the shift from didactic to active learning approaches. These findings may be useful for educators who are developing introductory pharmacokinetic courses by providing conceptual focus and revealing common misconceptions to explicitly address.
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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 | 0.016 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".