Adaptation of a Serious Illness Communication Training Intervention for the Rwandan Context
Bibliographic record
Abstract
CONTEXT: Communication skills are essential in cancer care, and international guidelines recommend communication training for all cancer care providers. Both clinical communication and training methods are influenced by culture. As oncology and palliative care expand globally, procedures are needed to adapt evidence-based communication training for diverse contexts. OBJECTIVE: To adapt a serious illness communication training intervention in Rwanda. METHODS: Guided by the Cultural Adaptation Process model, we conducted focus groups to understand communication training needs and gather feedback on a U.S. tool, the Serious Illness Conversation Guide (SICG). Based on identified needs, we made initial adaptations to an SICG-based training, incorporating tools from another U.S. program (VitalTalk). We piloted this training with 14 clinical psychologists, using lecture, demonstration, scripted roleplay, and small group discussion. Effectiveness was assessed through 5-point scales and qualitative feedback. RESULTS: Seventeen interdisciplinary oncology providers participated in one of three focus groups. While some had received lectures on communication, all believed additional training is needed. They endorsed the approach of adapting an international program rather than creating a Rwandan training de novo. Based on their input, we adapted and piloted a training that focused on three skills: 1) Set up the conversation and assess understanding; 2) Share information via a succinct "headline;" 3) Respond to emotion. Training methods received mean scores of 4.0 to 4.33 (5 = most effective). Further modifications were suggested to improve cultural concordance. CONCLUSION: Despite vast cultural differences, communication training interventions developed in the U.S. can be effectively adapted in African contexts through co-creation with local providers.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".