Discussing Serious News Remotely: Navigating Difficult Conversations During a Pandemic
Bibliographic record
Abstract
The 2020 severe acute respiratory syndrome coronavirus 2 pandemic has led to an increasing number of telemedicine clinician-patient encounters through telephone or videoconference. This provides a particular challenge in cancer care, where discussions frequently pertain to serious topics and are preferably performed in person. In this review, we use the SPIKES (Setting, Perception, Invitation, Knowledge, Empathy/Emotion, and Strategy/Summarize) protocol as a framework for how to approach the discussion of serious news through telemedicine. We discuss the practical and technical aspects of preparation for a remote conversation and review some differences, limitations, and advantages of these discussions. The greatest challenge with the medium is the loss of the ability to read and display nonverbal cues. Vigilant attention to proven communication strategies and solicitation of patient involvement with the discussion can allow the care provider to display empathy at a distance. Having serious discussions through telemedicine is likely unavoidable for many providers in this unprecedented time. This summary provides some strategies to help to maintain the high standard of care that we all seek for our patients who are receiving serious news.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".