Experiences and attitudes towards agitated behaviours in TBI ICU patients (EXSTATIC): understanding various management practices through qualitative interviews with nurses
Bibliographic record
Abstract
INTRODUCTION & OBJECTIVES: Agitation is a common complication after an acute TBI in ICU patients. Professionals have a range of strategies to address agitation. Yet the absence of evidence-based guidelines and how these strategies are implemented complicates the management and safety may often be compromised for both ICU professionals and patients. This project explores experiences and attitudes of ICU-nurses to better understand the management of agitated behaviors in acute TBI-patients. METHODS: Semi-structured interviews were conducted with 12 ICU-nurses from two Level-1 trauma centers in Canada. The interviews explored experiences and perceptions of managing agitation in critically ill TBI-patients. Interviews were analyzed using thematic analysis, facilitating the examination of how management practices interface with contextual variables and clinical strategies. RESULTS: Five themes were identified: (1) a variety of symptoms differing according to patient profile and time since awakening, (2) different agitation management approaches stem from different concerns, (3) strategies used by nurses to manage agitation, (4) contextual factors influence management, and (5) potential opportunities to improve integrated care model. CONCLUSIONS: This research describes nurses' perceptions and helps understand management of agitation, by considering underlying contexts and factors affecting TBI-agitated patients management, how ICU itself contributes to agitation and potential areas for improvement.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".