Determinants of Caregiver Well-Being in End-of-Life Care: A Systematic Review Protocol
Bibliographic record
Abstract
Abstract End-of-life caregiving represents one of the most demanding experiences for informal caregivers who often sacrifice their own well-being while supporting dying loved ones. This systematic review protocol presents a comprehensive framework for synthesizing evidence on factors influencing caregiver well-being in end-of-life care settings. Using the SPIDER framework (Sample, Phenomenon of Interest, Design, Evaluation, Research type), we will investigate caregiver experiences across physical, psychological, social, and spiritual well-being dimensions. Eight electronic databases (PubMed, Web of Science, Embase, CINAHL, MEDLINE, Cochrane Library, PsycINFO, Scopus) will be searched from inception to June 2025. Eligible studies include quantitative, qualitative, and mixed-methods research examining factors associated with caregiver well-being in hospice, palliative, and terminal care contexts. Covidence software will facilitate systematic screening, data extraction, and quality assessment by two independent reviewers using design-specific appraisal tools. Data synthesis will employ a convergent integrated approach combining narrative synthesis with meta-analysis where appropriate. Quality assessment will utilize JBI checklists, Newcastle-Ottawa Scale, and CASP tools, with evidence certainty evaluated using GRADE and GRADE-CERQual. This protocol follows PRISMA-P guidelines and will be registered with PROSPERO. Findings will inform evidence-based interventions and policies supporting the millions providing essential end-of-life care.
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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 | 0.106 | 0.102 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.018 | 0.016 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.102 | 0.014 |
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".