Using an “Escape Room” toolbox approach to enhance pharmacology education
Bibliographic record
Abstract
Background: Faculty are encouraged to use a variety of teaching/learning strategies to engage nursing students. While simulation and games are now common, there were no reports in the nursing literature using an “escape room” concept. Escape rooms use an entertainment approach as teams engage in critical thinking to solve puzzles and find clues to escape a room. In the classroom setting, this concept is modified to solve a mystery by finding various objects through a series of puzzles to locate clues. Some of these games involve finding numerical clues to open locks on a box, such as a toolbox. The purpose of this study was to describe the use of a toolbox gaming strategy based on an escape room concept to help students learn about cardiovascular medications in a pharmacology course.Methods: This pilot study employed a descriptive qualitative method to investigate an approach to pharmacology education. The sample consisted of first semester nursing students.Results: Student responses to criteria-based questions resulted in three themes: engaging, teamwork, and frustration, related to using a toolbox scenario strategy as a pathway to learning.Conclusions: This descriptive study yielded mixed results from the students who were frustrated by time constraints but engaged in the learning experience. Lessons are offered for future improvements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".