Prevalence and Factors of Intensive Care Unit Conflicts: The Conflicus Study
Bibliographic record
Abstract
RATIONALE: Many sources of conflict exist in intensive care units (ICUs). Few studies recorded the prevalence, characteristics, and risk factors for conflicts in ICUs. OBJECTIVES: To record the prevalence, characteristics, and risk factors for conflicts in ICUs. METHODS: One-day cross-sectional survey of ICU clinicians. Data on perceived conflicts in the week before the survey day were obtained from 7,498 ICU staff members (323 ICUs in 24 countries). MEASUREMENTS AND MAIN RESULTS: Conflicts were perceived by 5,268 (71.6%) respondents. Nurse-physician conflicts were the most common (32.6%), followed by conflicts among nurses (27.3%) and staff-relative conflicts (26.6%). The most common conflict-causing behaviors were personal animosity, mistrust, and communication gaps. During end-of-life care, the main sources of perceived conflict were lack of psychological support, absence of staff meetings, and problems with the decision-making process. Conflicts perceived as severe were reported by 3,974 (53%) respondents. Job strain was significantly associated with perceiving conflicts and with greater severity of perceived conflicts. Multivariate analysis identified 15 factors associated with perceived conflicts, of which 6 were potential targets for future intervention: staff working more than 40 h/wk, more than 15 ICU beds, caring for dying patients or providing pre- and postmortem care within the last week, symptom control not ensured jointly by physicians and nurses, and no routine unit-level meetings. CONCLUSIONS: Over 70% of ICU workers reported perceived conflicts, which were often considered severe and were significantly associated with job strain. Workload, inadequate communication, and end-of-life care emerged as important potential targets 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".