Using Skeletal Outlines and Illustrative Diagrams as a Teaching/Learning Strategy in a Baccalaureate Nursing Pharmacology Course
Bibliographic record
Abstract
Knowledge of pharmacology is an important aspect of safe, effective and competent clinical nursing practice, yet it can be challenging to teach. Many academics within the discipline of nursing are concerned that current nursing curricula may not be preparing nurses to accept responsibilities related to pharmacological management and are inadequately prepared for their role in medication administration. Nursing faculty who teach advanced clinical nursing practice courses also report that students lack the foundational knowledge of pharmacology needed and required in advanced clinical courses. These concerns have led to a call for additional focus that must be placed on student knowledge acquisition of pharmacology content due to poor student performance on standardized tests. Within a four-year undergraduate Bachelor of Science in Nursing (BScN) curriculum, pharmacology content was taught to first-term, second year students within a nursing praxis course, over a 13-week period. Students were required to understand the foundational principles, concepts and content of pharmacology and to integrate this knowledge in a critical reflective manner in clinical practice. A teaching/learning approach was implemented within the classroom that aligned with the work of Bui and McDaniel (2015). This method utilized outlines, illustrative diagrams and mental models aimed to enhance student note-taking in the development of content knowledge and to assist students to internalize critical cognitive learning processes. Consequently, student comprehension and critical thinking of pharmacology content was increased. Establishing a solid foundation of pharmacology that is integrated in a critically manner is a major component of providing safe and competent patient care.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".