Supporting Interprofessional Engagement in Serious Illness Conversations
Bibliographic record
Abstract
Communication is vital to quality palliative care nursing particularly when caring for someone with a chronic life-limiting illness and their family. Conversations about future decline and preferred care are considered challenging and difficult and are often avoided, resulting in missed opportunities for improving care. To support more, earlier, better conversations, health care organizations in British Columbia, Canada, adopted the Serious Illness Care Program inclusive of the Serious Illness Conversation Guide developed by Ariadne Labs. Workshops for interprofessional team members have been held throughout the province. Nurses and allied health identified the need for more guidance in using the guide in the contexts of their clinical practice. Specifically challenging has been prognosis communication that falls within the scope of practice for each profession. Informed by workshop feedback, an expert team of nurse clinicians and educators tailored an interprofessional clinician reference guide to optimize the guide's use across health care settings. In this article, we present the adaptations focusing on (1) the role of nurses and allied health in serious illness communication, (2) prognosis communication, and (3) a range of role-play scenarios specific to nonphysician practice for serious illness conversations that may arise within the process of 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 | 0.029 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".