Critical Care Outreach Team Nurses' Approaches to End‐of‐Life Conversations: An Interpretive Descriptive Study
Bibliographic record
Abstract
BACKGROUND: Critical care outreach team (CCOT) nurses are part of hospital emergency response teams, assessing and mobilising resources for decompensating patients. Although a significant proportion of this role involves addressing end-of-life (EOL) issues, this role is ill-defined in the literature and in practice. AIM: To explore the experiences of CCOT nurses with EOL conversations. STUDY DESIGN: A qualitative interpretive descriptive methodology guided the study. A semi-structured interview guide was used to collect audio-recorded individual interview data from CCOT nurses from seven hospitals in Ontario. Interview data were analysed using a thematic analysis approach. RESULTS: Data from 11 CCOT nurse participants revealed two key themes: 'Acquiring skills to discuss end-of-life issues' and 'Dynamic approaches to end-of-life conversations'. CCOT nurses' role in EOL conversations diverged significantly from their practice as bedside critical care nurses, assuming additional autonomy and responsibility in these conversations. Although CCOT nurses frequently had to navigate EOL discussions, they reported very little training to assist in acquisition of this skill. Despite no uniform education being provided to CCOT nurses, all nurses across multiple unrelated organisations reported using similar approaches. CONCLUSIONS: Findings from this study suggest that navigating EOL issues is an important learning need for CCOT nurses, pointing towards the need for more robust education at organisational, provincial and national levels. Additional research is recommended to quantify the participation of CCOT nurses in EOL discussions, expanding knowledge about the extent of their involvement. Further research is also warranted to delineate and support nursing scope of practice within this role, informing policy and guidelines. RELEVANCE TO CLINICAL PRACTICE: CCOT nurses encounter EOL issues in their daily practice. Additional education and increased role clarity are recommended to support nurses in this practice area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".