Communication of Code Status Escalation for Nurses and Physicians in the Intensive Care Unit: A Case Study
Bibliographic record
Abstract
BACKGROUND: Interprofessional teams working in the Intensive Care Unit (ICU) care for patients requiring varying degrees of life sustaining therapy. A patient's code status can help clinicians to understand the appropriate life support measures to deliver to patients in this setting. Members of the interprofessional team, such as physicians and nurses, can experience challenges related to communication when the code status is unclear. PURPOSE: The purpose of this study was to explore how nurses and physicians in the ICU experience communication of code status escalations. METHODS: A qualitative case study approach was used. Participants were physicians and nurses, working in the medical-surgical ICU of a large, urban academic hospital. Data were collected using semi-structured interviews, observations of health care rounds and a chart review. Data were analyzed using qualitative content analysis. RESULTS: Thematic findings include: (1) engaging in an interprofessional discussion, (2) finding consistent documentation, (3) revisiting the code status, and (4) telling the patient story. The study findings also provide contextual information about participants' experiences of code status communication during the first wave (February 2020 to May 2020) of the COVID-19 pandemic. CONCLUSIONS: The results of this study could inform standard communication frameworks or practices related to dissemination of code status decisions among members of the ICU team.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| 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".