A dimensional analysis of experienced intensive care unit nurses' clinical decision‐making for bleeding after cardiac surgery
Bibliographic record
Abstract
BACKGROUND: Bleeding following cardiac surgery is common and serious, yet a gap persists in understanding how experienced intensive care nurses identify and respond to such complications. AIM: To describe the clinical decision-making of experienced intensive care unit nurses in addressing bleeding after cardiac surgery. STUDY DESIGN: This qualitative study adopted the Recognition-Primed Decision Model as its theoretical framework. Thirty-nine experienced nurses from four adult intensive care units participated in semi-structured interviews based on the critical decision method. The interviews explored their clinical judgements and decisions in bleeding situations, and data were analysed through dimensional analysis, an alternative to grounded theory. RESULTS: Participants maintained consistent vigilance towards post-cardiac surgery bleeding, recognizing it through a haemorrhagic dimension associated with blood loss and chest drainage and a hypovolemic dimension focusing on the repercussions of reduced blood volume. These dimensions organized their understanding of bleeding types (i.e., normal, medical, surgical, tamponade) and necessary actions. Their decision-making encompassed monitoring bleeding, identifying the cause, stopping the bleeding, stabilizing haemodynamic and supporting the patient and family. Participants also adapted their actions to specific circumstances, including local practices, professional autonomy, interprofessional dynamics and resource availability. CONCLUSIONS: Nurses' decision-making was shaped by their personal attributes, the patient's condition and contextual circumstances, underscoring their expertise and pivotal role in anticipating actions and adapting to diverse conditions. The concept of actionability emerged as the central dimension explaining their decision-making, defined as the capability to implement actions towards specific goals within the possibilities and constraints of a situation. RELEVANCE TO CLINICAL PRACTICE: This study underscores the need for continual updates to care protocols to align with current evidence and for quality improvement initiatives to close existing practice gaps. Exploring the concept of actionability further, developing adaptability-focused educational programmes, and understanding decision-making intricacies are crucial for informing nursing education and decision-support systems.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".