Setting Regional Priorities for Palliative and End-of-Life Care Research Using a Delphi Technique Approach
Bibliographic record
Abstract
ObjectiveIdentifying research priorities is very important for palliative and end-of-life care to ensure research is focused on evidence gaps. This project aimed to identify and prioritise palliative and end-of-life care research areas within the West Midlands region in United Kingdom (UK).MethodsA modified Delphi technique approach was used with palliative care stakeholders. The first round was item generation via rapid interviews. Data were analysed using content analysis and all the items were grouped into main categories. For round two, an online survey was conducted to present all the items from round one, and stakeholders were asked to rate the priority of items on a Likert-type scale (1 = not a priority to 7 = essential priority). Items that achieved consensus in round two were presented to the third round, where stakeholders ranked them in descending order.ResultsWe completed and analysed 56 rapid interviews which resulted in 158 research items under 15 categories. The research items were rated by 30 stakeholders and seven items which reached consensus were subsequently ranked in order by 45 stakeholders. The highest ranked item was 'Integrated care systems to prevent crisis', followed by three research items related to 'equity' in palliative care.ConclusionsOur research priorities, although unique for our region, mirror previously research priorities from other regions and countries. This suggests issues of integration and equity in palliative and end-of-life care remain unresolved, despite ongoing initiatives and research to address these issues.
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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 | 0.071 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".