Content of Tele-Palliative Care Consultations with Patients Receiving Dialysis
Bibliographic record
Abstract
Background: Little is known about the content of communication in palliative care telehealth conversations in the dialysis population. Understanding the content and process of these conversations may lead to insights about how palliative care improves quality of life. Methods: We conducted a qualitative analysis of video recordings obtained during a pilot palliative teleconsultation program. We recruited patients receiving dialysis from five facilities affiliated with an academic medical center. Palliative care clinicians conducted teleconsultation using a wall-mounted screen with a camera mounted on a pole and positioned mid-screen in the line of sight to facilitate direct eye contact. Patients used an iPad that was attached to an IV pole positioned next to the dialysis chair. Conversations were coded using a preexisting framework of themes and content from the Serious Illness Conversation Guide (SICG) and revised Edmonton Symptom Assessment System—Renal. Results: We recruited 39 patients to undergo a telepalliative care consultation while receiving dialysis, 34 of whom completed the teleconsultation. Specialty palliative care clinicians (3 physicians and 1 nurse practitioner) conducted 35 visits with 34 patients. Median (interquartile range) duration of conversation was 42 (28–57) minutes. Most frequently discussed content included sources of strength (91%), critical abilities (88%), illness understanding (85%), fears and worries (85%), what family knows (85%), fatigue (77%), and pain (65%). Process features such as summarizing statements (85%) and making a recommendation (82%) were common, whereas connectional silence (56%), and emotion expression (21%) occurred less often. Conclusions: Unscripted palliative care conversations in outpatient dialysis units through telemedicine exhibited many domains recommended by the SICG, with less frequent discussion of symptoms. Emotion expression was uncommon for these conversations that occurred in an open setting.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".