MORECare research methods guidance development: Recommendations for ethical issues in palliative and end-of-life care research
Bibliographic record
Abstract
BACKGROUND: There is little guidance on the particular ethical concerns that research raises with a palliative care population. AIM: To present the process and outcomes of a workshop and consensus exercise on agreed best practice to accommodate ethical issues in research on palliative care. DESIGN: Consultation workshop using the MORECare Transparent Expert Consultation approach. Prior to workshops, participants were sent overviews of ethical issues in palliative care. Following the workshop, nominal group techniques were used to produce candidate recommendations. These were rated online by participating experts. Descriptive statistics were used to analyse agreement and consensus. Narrative comments were collated. SETTING/PARTICIPANTS: Experts in ethical issues and palliative care research were invited to the Cicely Saunders Institute in London. They included senior researchers, service providers, commissioners, researchers, members of ethics committees and policy makers. RESULTS: The workshop comprised 28 participants. A total of 16 recommendations were developed. There was high agreement on the issue of research participation and high to moderate agreement on applications to research ethics committees. The recommendations on obtaining and maintaining consent from patients and families were the most contentious. Nine recommendations were refined on the basis of the comments from the online consultation. CONCLUSIONS: The culture surrounding palliative care research needs to change by fostering collaborative approaches between all those involved in the research process. Changes to the legal framework governing the research process are required to enhance the ethical conduct of research in palliative care. The recommendations are relevant to all areas of research involving vulnerable adults.
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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 | 0.559 | 0.665 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.006 | 0.015 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.017 | 0.021 |
| Research integrity | 0.039 | 0.031 |
| Insufficient payload (model declined to judge) | 0.072 | 0.045 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".