Priorities for global research into children’s palliative care: results of an International Delphi Study
Bibliographic record
Abstract
BACKGROUND: There is an urgent need to develop an evidence base for children's palliative care (CPC) globally, and in particular in resource-limited settings. Whilst the volume of CPC research has increased in the last decade, it has not been focused on countries where the burden of disease is highest. For example, a review of CPC literature in sub Saharan Africa (SSA) found only five peer-reviewed papers on CPC. This lack of evidence is not confined to SSA, but can be seen globally in specific areas, such as an insufficient research and evidence base on the treatment of pain and other symptoms in children. This need for an evidence base for CPC has been recognised for some time, however without understanding the priorities for research in CPC organisations, many struggle with how to allocate scarce resources to research. METHOD: The International Children's Palliative Care Network (ICPCN) undertook a Delphi study between October 2012 and February 2013 in order to identify the global research priorities for CPC. Members of the ICPCN Scientific Committee formed a project working group and were asked to suggest areas of research that they considered to be important. The list of 70 areas for research was put through two rounds of the Delphi process via a web-based questionnaire. ICPCN members and affiliated stakeholders (n = 153 from round 1 and n = 95 from round 2) completed the survey. Participants from SSA were the second largest group of respondents (28.1 % round 1, 24.2 % round 2) followed by Europe. RESULTS: A list of 26 research areas reached consensus. The top five priorities were: Children's understanding of death and dying; Managing pain in children where there is no morphine; Funding; Training; and Assessment of the WHO two-step analgesic ladder for pain management in children. CONCLUSIONS: Information from this study is important for policy makers, educators, advocates, funding agencies, and governments. Priorities for research pertinent to CPC throughout the world have been identified. This provides a much needed starting place for the allocation of funds and building research infrastructure. Researchers working in CPC are in a unique position to collaborate and produce the evidence that is needed.
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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 | 0.134 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".