In-Person and Virtual Adaptation of an Interprofessional Palliative Care Communications Skills Training Course for Pediatric Oncology Clinicians
Bibliographic record
Abstract
Introduction Empathic communication is crucial for clinicians when discussing palliative and end-of-life (PC/EOL) care with parents of children with cancer. Unfortunately, many parents report inadequate communication at these distressing times. This study evaluates the communication skills training (CST) clinicians received to deliver a PC/EOL communication intervention as part of a multi-site randomized-controlled trial (RCT). Training was provided using an in-person format and then adapted to a virtual platform to accommodate remote learners. Methods Clinicians were trained in dyads (one physician and one nurse [RN] or advanced practice provider [APP]) over 3 days (in-person or virtually). Four pediatric oncology cases were developed and each incorporated three timepoints: diagnosis, disease progression, and end-of life. Training was adapted from VitalTalkTM and included didactic instruction, videos, visual aids, and role play. Participants completed a confidential, post-training survey. A self-reported quality assurance checklist measured fidelity to the intervention during the RCT. Results Thirty clinicians completed training; 26 completed post-training surveys including 46.1% physicians, 30.8% RNs and 23.1% APPs. Most were female (65.4%); white (80.8%), and 40-50 years old (53.9%). Nine (34.6%) trained in-person; the rest trained virtually. Ninety-two percent reported the course was valuable/very valuable for developing PC/EOL communication skills and 96% learned something new. Dyads trained virtually had similar fidelity to those trained in-person (95% and 90% respectively) when delivering the intervention to parents. Discussion This PC/EOL CST, implemented in-person and virtually, was valuable for improving pediatric oncology clinicians’ communication skills and was translated effectively into practice.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".