Methodological and ethical challenges in designing and conducting research at the end of life: A systematic review of qualitative and textual evidence
Bibliographic record
Abstract
AIM: This systematic review aims to identify methodological and ethical challenges in designing and conducting research at the end of life from the perspective of researchers and provide a set of recommendations. BACKGROUND: Conducting research with patients and family carers facing end-of-life issues is ethically and methodologically complex. DESIGN: A systematic review was conducted. DATA SOURCES: Four databases (MEDLINE, EMBASE, CINAHL, PsycInfo) were searched from inception until the end of 2021 in February 2022. REVIEW METHODS: The Preferred Reporting Items for Systematic Reviews was followed, and the JBI Approach to qualitative synthesis was used for analysis. RESULTS: Seventeen of 1983 studies met inclusion criteria. Data were distilled to six main themes. These included (1) the need for flexibility at all stages of the research process; (2) careful attention to timing; (3) sensitivity in approach; (4) the importance of stakeholder collaboration; (5) the need for unique researcher skills; and (6) the need to deal with the issue of missing data. CONCLUSION: The findings illuminate several considerations that can inform training programmes, ethical review processes and research designs when embarking on research in this field.
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 | 0.315 | 0.512 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".