How to Advance Palliative Care Research in South America? Findings From a Delphi Study
Bibliographic record
Abstract
CONTEXT: Progress in palliative care (PC) necessarily involves scientific development. However, research conducted in South America (SA) needs to be improved. OBJECTIVES: To develop a set of recommendations to advance PC research in SA. METHODS: Eighteen international PC experts participated in a Delphi study. In round one, items were developed (open-ended questions); in round two, each expert scored the importance of each item (from 0 to 10); in round three, they selected the 20 most relevant items. Throughout the rounds, the five main priority themes for research in SA were defined. In Round three, consensus was defined as an agreement of ≥75%. RESULTS: 60 potential suggestions for overcoming research barriers in PC were developed in round one. Also in Round one, 88.2% (15 of 17) of the experts agreed to define a priority research agenda. In Round two, the 36 most relevant suggestions were defined and a new one added. Potential research priorities were investigated (open-ended). In Round three, from the 37 items, 10 were considered the most important. Regarding research priorities, symptom control, PC in primary care, public policies, education and prognosis were defined as the most relevant. CONCLUSION: Potential strategies to improve scientific research on PC in SA were defined, including stimulating the formation of collaborative research networks, offering courses and workshops on research, structuring centers with infrastructure resources and trained researchers, and lobbying governmental organizations to convince about the importance of palliative care. In addition, priority research topics were identified in the region.
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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 | 0.084 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| 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".