Team situation awareness and the anticipation of patient progress during ICU rounds
Bibliographic record
Abstract
BACKGROUND: The ability of medical teams to develop and maintain team situation awareness (team SA) is crucial for patient safety. Limited research has investigated team SA within clinical environments. This study reports the development of a method for investigating team SA during the intensive care unit (ICU) round and describes the results. METHODS: In one ICU, a sample of doctors and nurses (n = 44, who combined to form 37 different teams) were observed during 34 morning ward rounds. Following the clinical review of each patient (n = 105), team members individually recorded their anticipations for expected patient developments over 48 h. Patient-outcome data were collected to determine the accuracy of anticipations. Anticipations were compared among ICU team members, and the degree of consensus was used as a proxy measure of team SA. Self-report and observational data measured team-member involvement and communication during patient reviews. RESULTS: For over half of 105 patients, ICU team members formed conflicting anticipations as to whether patients would deteriorate within 48 h. Senior doctors were most accurate in their predictions. Exploratory analysis found that team processes did not predict team SA. However, the involvement of junior and senior trainee doctors in the patient decision-making process predicted the extent to which those team members formed team SA with senior doctors. CONCLUSIONS: A new method for measuring team SA during the ICU round was successfully employed. A number of areas for future research were identified, including refinement of the situation awareness and teamwork measures.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".