Effect of an active teaching method on writing drug prescriptions applied to nursing students: a quasi-experimental study
Bibliographic record
Abstract
BACKGROUND: In Brazil, as well as in several other countries, e.g. United Kingdom, Ireland, Netherlands, Australia, Canada and New Zealand, nurses are legally authorised to prescribe medications, yet this practice remains under-explored in Primary Health Care. Active teaching methods can be employed to enhance drug prescriptions writing skills in undergraduate Nursing Education. AIM: The aim of this study was to evaluate the effectiveness of an active teaching method and the situational motivation for learning drug prescriptions writing in Primary Health Care, applied to Brazilian undergraduate Nursing students. METHODS: A quasi-experimental study with a single-group pretest/posttest design was conducted from September to November 2024. The study was conducted in the Nursing Department of a Brazilian public university with a convenience sample of 54 undergraduate Nursing students. The intervention followed the "Good Drug Prescriptions Writing Practices Teaching Method", by which students produced drug prescriptions, and their quality was assessed. Situational motivation for learning was also evaluated. RESULTS: The intervention significantly improved the quality of drug prescriptions (p < 0.001), with immediate post-training effects sustained over time, indicating durability. For situational motivation, the intervention had a positive (p = 0.029) - though nonuniform - impact on overall motivation (p < 0.001). CONCLUSIONS: Implementing this intervention in Nursing Education fostered competency in drug prescriptions writing, directly contributing to strengthening public health and the consolidation of safe, evidence-based practices in healthcare systems. TRIAL REGISTRATION: The study was registered in the Brazilian Registry of Clinical Trials (ReBEC) in the https://ensaiosclinicos.gov.br/rg/RBR-5mwfczh under ID code (RBR-5mwfczh) aproved in 11 november of 2024, retrospectively registered.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".