How do healthcare practitioners talk about end-of-life conversations? A poetic inquiry
Bibliographic record
Abstract
Despite agreement that end-of-life conversations should happen early on in the illness trajectory, it is widely acknowledged that healthcare practitioners often engage in these conversations when death is imminent or avoid the conversation altogether. Healthcare practitioners’ feelings of distress influence how end-of-life conversations are approached, yet thorough exploration of this emotional experience and its impact are largely missing from the literature. The aims of this preliminary scoping literature review using poetic inquiry were to examine physicians’ and nurses’ emotional distress in their accounts of how they approach end-of-life conversations, and to map key concepts relevant to exploring barriers to these conversations. The poetic findings highlight the differing nature of distress for physicians and nurses. Physicians’ distress appears to stem from adhering to their role of ‘curer’ when communicating with terminally ill adult patients at the end of life, whereas the sources of nurses’ distress appear to be interprofessional hierarchies and conflicts. Future research and training that uses methods to decentre and disrupt hierarchies and ingrained practices will be important to nursing practice and in improving end-of-life conversations. Arts-based approaches are one such method that could be pursued.
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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 | 0.089 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.016 | 0.038 |
| Scholarly communication | 0.021 | 0.044 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".