489 An innovative hybrid palliative care fellowship program: empowering pediatricians and enhancing care for children in resource-limited settings
Bibliographic record
Abstract
Objective To describe the development and implementation of a 1-year ‘Hybrid’ Pediatric Palliative Care (PPC) Fellowship, which includes both clinical and online learning to train paediatricians as specialists and leaders in paediatric palliative care in South and Southeast Asia. Methods Globally, 97% of children needing palliative care (PC) live in low- or middle-income countries, where access is often very limited. In India, less than 1% of the 1.6 million children needing PC can access these services. Building capacity and training opportunities for paediatricians are essential to improve access to PC in these settings. The Hybrid fellowship was developed through the existing partnership between Two Worlds Cancer Collaboration, Canada and the Hyderabad Centre for Palliative Care to train paediatricians as specialists and leaders in paediatric palliative care in South and Southeast Asia. A team of PPC experts developed the fellowship after a rigorous review of relevant literature and educational materials, incorporating their experiences running PPC traditional (in-person) fellowship programs in both high and lower-resourced settings. Results The fellowship includes formal teaching, clinical rotations, mentorship, regular assessments of trainees, and a scholarly project. Teaching includes 100 hours of weekly online classes, with a focus on case-based learning and leadership skills. Mandatory 4 months of clinical rotations in PC includes 2 months in the regional centre of PPC excellence in Hyderabad, India. Trainees’ progression towards program competencies is assessed through written and observed standardised clinical examinations. A mentorship program provides additional support, which continues beyond the fellowship through an early career mentorship group. As a part of research and quality improvement (QI) training, fellows complete a scholarly project with support and supervision from experienced research mentors. More than 30 regional and international PPC experts contribute to the program as faculty. Since 2021, 6 paediatricians have completed the ‘Hybrid’ fellowship, from India (2), Bangladesh (1), and the Philippines (3), with 3 fellows currently in training. Graduated fellows have become regional and national leaders in PPC, developing new PPC programs, and implementing new PPC training in their home country. The program successfully sought endorsement from the Royal College of Paediatrics and Child Health (UK), which has strengthened the program’s rigor and quality. Conclusion A 1-year Hybrid PPC Fellowship, which trains pediatricians to become specialists and champions of PPC, is an innovative model that can successfully build PPC capacity in resource-limited settings and increase access to PPC for children with life-limiting illnesses.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".