Evaluating complex interventions in End of Life Care: the MORECare Statement on good practice generated by a synthesis of transparent expert consultations and systematic reviews
Bibliographic record
Abstract
BACKGROUND: Despite being a core business of medicine, end of life care (EoLC) is neglected. It is hampered by research that is difficult to conduct with no common standards. We aimed to develop evidence-based guidance on the best methods for the design and conduct of research on EoLC to further knowledge in the field. METHODS: The Methods Of Researching End of life Care (MORECare) project built on the Medical Research Council guidance on the development and evaluation of complex circumstances. We conducted systematic literature reviews, transparent expert consultations (TEC) involving consensus methods of nominal group and online voting, and stakeholder workshops to identify challenges and best practice in EoLC research, including: participation recruitment, ethics, attrition, integration of mixed methods, complex outcomes and economic evaluation. We synthesised all findings to develop a guidance statement on the best methods to research EoLC. RESULTS: We integrated data from three systematic reviews and five TECs with 133 online responses. We recommend research designs extending beyond randomised trials and encompassing mixed methods. Patients and families value participation in research, and consumer or patient collaboration in developing studies can resolve some ethical concerns. It is ethically desirable to offer patients and families the opportunity to participate in research. Outcome measures should be short, responsive to change and ideally used for both clinical practice and research. Attrition should be anticipated in studies and may affirm inclusion of the relevant population, but careful reporting is necessitated using a new classification. Eventual implementation requires consideration at all stages of the project. CONCLUSIONS: The MORECare statement provides 36 best practice solutions for research evaluating services and treatments in EoLC to improve study quality and set the standard for future research. The statement may be used alongside existing statements and provides a first step in setting common, much needed standards for evaluative research in EoLC. These are relevant to those undertaking research, trainee researchers, research funders, ethical committees and editors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.749 | 0.809 |
| Meta-epidemiology (narrow) | 0.007 | 0.010 |
| Meta-epidemiology (broad) | 0.020 | 0.023 |
| Bibliometrics | 0.032 | 0.024 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.030 | 0.019 |
| Open science | 0.014 | 0.033 |
| Research integrity | 0.045 | 0.028 |
| Insufficient payload (model declined to judge) | 0.005 | 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".