Communication in Palliative Care and About End of Life: A State-of-the-Art Literature Review of Conversation-Analytic Research in Healthcare
Bibliographic record
Abstract
I review conversation analytic research on healthcare interactions in palliative care, and end-of-life preferences and plans conversations in other settings.The review process drew on established systematic review methods.Twenty-two publications were included.Key themes were initiating and managing the topics of dying, of prognosis, and of advance care planning, and interactionally managing emotions.There is substantial, cumulative evidence about patterns and practices for initiating sensitive conversations, managing emotions, and indirectly referring to death.Two of 22 studies examined interactional consequences of companions accompanying patients, and two examined pain assessment.Current evidence is restricted: 21 of 22 studies were in secondary/tertiary care and all were in high-income countries, and most involved specialist physicians.Nevertheless, findings contribute to conversation analytic scholarship on delicacy, emotion in interaction, and indirect reference.Healthcare applications include contributions to training in communicating about sensitive topics and to policy on talking about dying.Data presented are in British English, U.S. English, Canadian English, Swiss-French, and Japanese. Overview and backgroundThis state-of-the-art review examines published findings from conversation analytic research on palliative and end of life (EoL) healthcare interactions.The studies reviewed examine recurrent difficulties entailed in these high-stakes, important conversations-conversations that are often daunting for all parties involved.Most of this work aims to describe and understand interactional means by which people limit, circumvent, or overcome these difficulties, and thereby achieve palliative care and EoL conversations of substance and sensitivity.A small number of the studies examine ways in which healthcare professionals (HCPs) can exacerbate the difficulties entailed.My focus is on interactions in which people approach and address current and future states of affairs and events that are arguably the most painful, distressing, frightening, and-in a senseunknowable matters that humans deal with in our lives.Besides being emotionally charged, other interactional challenges arise.These include uncertainties about at least three matters: how long someone will live, the precise nature and course of future symptoms, and the availability and
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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 | 0.028 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".