Investigating clinical decision-making in bleeding complications among nursing students: A longitudinal mixed-methods study
Bibliographic record
Abstract
AIM: To describe undergraduate nursing students' clinical decision-making in post-procedural bleeding scenarios and explore the changes from the first to the final year of their program. BACKGROUND: Bleeding is a common complication following invasive procedures and its effective management requires nurses to develop strong clinical decision-making competencies. Although nursing education programs typically address bleeding complications, there is a gap in understanding how nursing students make clinical decisions regarding these scenarios. Additionally, little is known about how their approach to bleeding management evolves over the course of their education. DESIGN: Longitudinal mixed-methods study based on the Recognition-Primed Decision Model. METHODS: A total of 59 undergraduate students recorded their responses to two clinical decision-making vignettes depicting patients with signs of bleeding post-hip surgery (first year) and cardiac catheterization (final year). Their responses were analyzed using content analysis. The resulting categories capture the cues students noticed, the goals they aimed to achieve, the actions they proposed and their expectations for how the bleeding situations might unfold. Code frequencies showing the most variation between the first and final years were analyzed to explore changes in students' clinical decision-making. RESULTS: Nearly all students focused on two primary categories: 'Bleeding' and 'Instability and Shock.' Fewer students addressed six secondary categories: 'Stress and Concern,' 'Pain,' 'Lifestyle and Social History,' 'Wound Infection,' 'Arrhythmia,' and 'Generalities in Surgery.' Students often concentrated on actions to manage bleeding without further assessing its causes. Changes from the first to the final year included a more focused assessment of instability and shifts in preferred actions. CONCLUSIONS: This study reveals that nursing students often prioritize immediate actions to stop bleeding while sometimes overlooking the assessment of underlying causes or broader care goals. It suggests that concept-based learning and reflection on long-term outcomes could improve clinical decision-making in post-procedural care.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".