A pilot assessment: Integrating a cystic fibrosis simulation scenario to enhance pre-licensure educational understanding of genomics
Bibliographic record
Abstract
Background and objective: Integration of patient simulations into the nursing student curricula have been shown to be effective and innovative teaching enhancements leading to enhanced knowledge, clinical reasoning and judgment for students, whilst promoting optimal patient care. This pilot study aimed to explore how the use of a simulation, with a genetic component of a Cystic Fibrosis (CF) case scenario, improved the self-perceived knowledge comprehension of pre-licensure baccalaureate nursing students of a large diverse urban School of Nursing.Methods: Three assessment surveys were utilized to glean data: nine multiple choice questions explored factual content of CF pre/post simulation; five question survey explored self-perception of knowledge and one open-ended simplified critical incident report provided qualitative data.Results: Twenty-four pre-licensure third year nursing students participated (three groups of eight students). All participants agreed that their understanding of the genetic component of CF improved post simulation. Four major themes emerged from the qualitative data: genomics and nursing; patient education; teamwork exercise and patient-nurse relationship. Conclusions: Integrating a genetically-based condition into a simulation, whereby students are expected to research the condition, engage in patient education, facilitate effective and appropriate nursing care enriches their critical thinking, confidence, skills and knowledge acquisition.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".