Does teaching methodology affect medication dosage calculation skills of undergraduate nursing students?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it