Does teaching methodology affect medication dosage calculation skills of undergraduate nursing students?
Bibliographic record
Abstract
One of the most critical functions of a nurse is the safe administration of medications. To ensure patient safety, nurses must be competent in medication dosage calculation (MDC) skills. It is imperative that nursing educators discover the most effective teaching methodology to ensure the greatest level of competency in MDC skills. The purpose of this causal-comparative quantitative study was to compare the effects of two teaching methodologies on senior-level nursing students’ completion of program MDC requirements, mathematics self-efficacy, and MDC competency at program end. The sample consisted of 94 senior-level bachelor’s degree nursing students from a southeastern United States university in the spring of 2015. Each participant completed a demographic questionnaire, Mathematics Self-Efficacy Scale (MSES), and MDC competency exam. Participants were assigned to one of two groups based on whether the participants completed MDC education in a stand-alone course or throughout the curriculum through self-learning modules. Chi-square and independent t-test results indicated that there were no statistical differences between the two groups (stand-alone course vs. self-learning modules) and ability to complete program MDC requirements, MSES scores, and MDC competency exam scores at program end. Data analysis using Chi-square and Fisher’s Exact tests indicated a statistically significant, but weak, correlation between MSES scores and MDC competency exam scores. Findings from this study indicate teaching MDC to nursing students using a stand-alone course versus self-learning modules produces the same results in the students’ ability to complete program MDC requirements, mathematics self-efficacy, and MDC competency at program end.
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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 | 0.003 | 0.019 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".