Using Virtual Learning to Develop Palliative Care Skills Among Humanitarian Health Workers in the Rohingya Refugee Response in Bangladesh
Bibliographic record
Abstract
Background: Palliative care is an essential component of health responses in humanitarian settings, yet it remains largely unavailable in these settings, due to limited availability of palliative care training for healthcare professionals. Online training programs which connect experts to clinicians in the field have been proposed as an innovative strategy to build palliative care capacity humanitarian settings. Objective: To describe the implementation and evaluate the impact of delivering palliative care education using an established virtual learning model (Project ECHO) for healthcare clinicians working in the Rohingya refugee response in Bangladesh. Program acceptability and the impacts on learners' self-reported knowledge, comfort, and practice changes were evaluated. Methods: Using the Project ECHO model, an education program consisting of 7 core sessions and monthly mentoring sessions was developed. Each session included a didactic lecture, case presentation and interactive discussion. Surveys of participants were conducted before and after the program to assess knowledge, confidence, and attitudes about palliative care as well as learning experiences from the program. Results: This virtual palliative care training program engaged 250 clinicians, including nurses (35%), medical assistants (28%) and physicians (20%). Most participants rated the program as a valuable learning experience (96%) that they would recommend to their colleagues (98%). Participants reported improvements in their knowledge and comfort related to palliative care after participation in the program, and had improved attitudes towards palliative care with demonstrated statistical significance (p < 0.05). Conclusions: Virtual training is a feasible model to support healthcare providers in a humanitarian health response. Project ECHO can help to address the urgent need for palliative care in humanitarian responses by supporting healthcare workers to provide essential palliative care to the growing number of individuals with serious health-related suffering in humanitarian settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".