An agenda to develop Pediatric Palliative care programs to serve children with life-threatening and life-limiting conditions in the Gulf Cooperation Council countries
Bibliographic record
Abstract
Medical advances have increased the number of children living with life-threatening/life-limiting illnesses worldwide, including in Gulf Cooperation Council (GCC) countries. Pediatric palliative care (PPC) is a relatively young subspecialty that cares for children with life-threatening/life-limiting illnesses and their families. PPC aims to alleviate physical, psychological, and social distress in children with life-threatening/life-limiting illnesses and their families and improve their quality of life. PPC is an essential service that should be implemented in all nations, as it is a human right. Moreover, a core value of PPC services is to alleviate children's suffering, irrespective of cure availability. Hence, the global consensus on palliative services must be universal and include developing countries with limited resources. While PPC services are growing internationally, the GCC countries have yet to implement these valuable services in the region. This work aims to define the local base information important to facilitating the PPC program. We explored and identified the information vital for establishing a successful program, which was then categorized and mapped into subgroups. In doing so, we outline a roadmap to facilitate the smooth introduction of PPC in GCC countries to benefit the lives of children with life-limiting illnesses.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".