Managing Anticipatory Grief in Family and Partners: A Systematic Review and Qualitative Meta-Synthesis
Bibliographic record
Abstract
Almost every person is affected by grief at some point during their lifetime. However, the majority of grief research has focused on the experiences and perspectives of individuals after loss. Grief can be expected or unexpected depending on the nature of loved one’s death. Like postdeath grief, individuals who expect the impending loss of a loved one may feel uncertainty, fear, and sadness, which can lead to a number of adverse outcomes on their health. Some research labels the predeath experience as anticipatory grief (AG). In this qualitative systematic review, we analyze 13 studies to examine how caregivers of terminally ill patients experience AG. First, we identify the four stages of AG: time of diagnosis, transition to hospice care, nearing death, and the moment of death. We highlight the characteristics of each stage and the coping mechanisms that family used to navigate them. Second, we discuss how AG influences family and partner roles and responsibilities. We also examine the interplay between caregiving motivations and activities, and the four stages of AG. We consider the relationship between AG, caregiving, and postdeath adjustment, including physical and mental health outcomes.
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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 | 0.021 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".