Clinical Reasoning to Advance Medication Safety by Senior Nursing Students
Bibliographic record
Abstract
Nursing students in their final year of their nursing education program are expected to administer medications to patients safely and competently. Currently, there is a lack of research with regard to how senior nursing students are using clinical reasoning to support medication safety in the clinical setting. This qualitative descriptive case study explored how senior nursing students applied critical thinking and clinical reasoning to support medication safety in their final clinical practicums. The study took place in 2019 and consisted of 13 face-to-face interviews with senior nursing students in their final clinical rotation. Six themes emerged from the interviews. Students described (1) administering medications safely by recognizing and clustering cues, (2) administering medications safely to the right patient in the context of care, (3) administering medications safely by determining the correct action, (4) administering medications safely to patients for the right reason, (5) reflecting on clinical reasoning experiences that supported medication safety, and (6) unit culture impact clinical reasoning about medication safety. Nursing students described how they used their clinical reasoning to support safe medication management and administration in clinical settings. Based on the findings from this study, we recommend that nursing education programs enhance opportunities for students to develop and reflect on their clinical reasoning about safe medication administration in clinical settings.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".