An Exploratory Investigation into the Roles of Critical Care Response Teams in End-of-Life Care
Bibliographic record
Abstract
BACKGROUND: Critical Care Response Teams (CCRTs) represent an important interface between end-of-life care (EOLC) and critical care medicine (CCM). The aim of this study was to explore the roles and interactions of CCRTs in the provision of EOLC from the perspective of CCRT members. METHODS: Twelve registered nurses (RNs) and four respiratory therapists (RTs) took part in focus groups, and one-on-one interviews were conducted with six critical care physicians. Thematic coding using a modified constructivist grounded theory approach was used to identify emerging themes through an iterative process involving a four-member coding team. RESULTS: Three main perspectives were identified that spoke to CCRT interactions and perceptions of EOLC encounters. CCRT members felt that they provide a unique skill set of multidisciplinary expertise in treating critically ill patients and evaluating the utility of intensive care treatments. However, despite feeling that they possessed the skills and resources to deliver quality EOLC, CCRT members were ambivalent with respect to whether EOLC was a part of their mandate. Challenges were also identified that impacted the ability of CCRTs to deliver quality EOLC. CONCLUSIONS: This research aids in understanding for the first time CCRT roles in EOLC from the perspectives of individual CCRT members themselves. While CCRTs provide unique multidisciplinary expertise to evaluate the utility of intensive care treatments, opportunities exist to support CCRTs in EOLC, such as dedicated EOLC training, protocols for advance care planning, documentation, and transitions to palliative care.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".