Exploring Clinical Decision-Making Competencies of Emergency Nurses in Trauma Care in Indonesia: Qualitative Study
Bibliographic record
Abstract
Background: Clinical decision-making is vital for emergency nurses, especially in trauma care that requires swift, accurate actions. In Indonesia, where resources are limited, little is known about how nurses manage such challenges. Objective: This study aimed to explore the clinical decision-making competencies of emergency nurses in trauma care, focusing on challenges, strategies, and influencing factors. Methods: This was a qualitative study using semi-structured interviews with 16 emergency nurses, complemented by observations and document analyses. Data were analyzed thematically, with triangulation, thereby ensuring validity. Results: Six key themes emerged: (1) recognize cues; (2) analyze cues; (3) prioritize hypothesis; (4) generate solutions; (5) take actions; and (6) evaluate outcomes. These highlight the adaptive and multidimensional nature of decision-making in emergencies. Conclusions: The decision-making of emergency nurses integrates analysis, prioritization, collaboration, and reflection. Enhanced training, such as simulation-based learning, and addressing systemic barriers can improve competencies. Policymakers should provide adequate resources and robust standards to support nurses under pressure.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".