Case studies in a flipped classroom: An approach to support nursing learning in pharmacology and pathophysiology.
Bibliographic record
Abstract
Background: Comprehensive understanding of pharmacology and pathophysiology is required for safe and effective use of medications in patient care. Case studies are an active learning strategy that can develop higher level learning. The use of case studies as a learning strategy in pharmacology and pathophysiology has not been assessed in nursing students. Methods: Undergraduate nursing students were surveyed to determine their perceptions of the use of case studies as a learning strategy in pharmacology and pathophysiology. Average responses to statements created for the study were measured using a Likert scale and differences were determined using ANOVA. Exploratory Factor Analysis of the data was performed. Results: Participants reported that the utilization of case studies enhanced knowledge acquisition and application in pharmacology and pathophysiology. Participants recommended the use of case studies as a learning strategy. Factor analysis produced two factors. Factor 1 was designated self-efficacy and critical reasoning around pathophysiology and pharmacology in patient care. Factor 2 was designated as attitude toward the learning model. Conclusion: Case studies engage students and are a potential tool for effective nursing education. Nursing students believe that case studies help to develop higher level learning when studying pharmacology and pathophysiology. A tool was developed that demonstrates potential for accurately measuring student attitudes towards a learning strategy and the impact of the learning strategy on nursing students’ self-efficacy related to pharmacology and pathophysiology.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".