Mixed Methods Research in the Development and Evaluation of Complex Interventions in Palliative and End-of-Life Care: Report on the MORECare Consensus Exercise
Bibliographic record
Abstract
BACKGROUND: Complex interventions are common in palliative and end-of-life care. Mixed methods approaches sit well within the multiphase model of complex intervention development and evaluation. Generic mixed methods guidance is useful but additional challenges in the research design and operationalization within palliative and end-of-life care may have an impact on the use of mixed methods. OBJECTIVE: The objective of the study was to develop guidance on the best methods for combining quantitative and qualitative methods for health and social care intervention development and evaluation in palliative and end-of-life care. METHODS: A one-day workshop was held where experts participated in facilitated groups using Transparent Expert Consultation to generate items for potential recommendations. Agreement and consensus were then sought on nine draft recommendations (DRs) in a follow-up exercise. RESULTS: There was at least moderate agreement with most of the DRs, although consensus was low. Strongest agreement was with DR1 (usefulness of mixed methods to palliative and end-of-life care) and DR5 (importance of attention to respondent burden), and least agreement was with DR2 (use of theoretical perspectives) and DR6 (therapeutic effects of research interviews). Narrative comments enabled recommendation refinement. Two fully endorsed, five partially endorsed, and two refined DRs emerged. The relationship of these nine to six key challenges of palliative and end-of-life care research was analyzed. CONCLUSIONS: There is a need for further discussion of these recommendations and their contribution to methodology. The recommendations should be considered when designing and operationalizing mixed methods studies of complex interventions in palliative care, and because they may have wider relevance, should be considered for other applications.
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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 | 0.718 | 0.614 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.011 | 0.029 |
| Research integrity | 0.018 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".