How do researchers conceive of spousal grief after cancer? A systematic review of models used by researchers to study spousal grief in the cancer context
Bibliographic record
Abstract
BACKGROUND: Although spouses bereaved after cancer are considered vulnerable people, there have been few empirical studies to explore grief specifically in this context. METHODS: Using PsycINFO, Medline, and the PRISMA statement, we systematically searched the literature by intersecting 'cancer' and 'grie*', 'cancer' and 'bereave*', and 'cancer' and 'mourn*'. RESULTS: Gathering 76 studies (2000-2013) that met the inclusion criteria for bereavement in adulthood, bereavement of an adult loved one and evidence-based research, we found the following: Spousal relationships are not systematically examined in the current dominant models of grief. Theoretically derived determinants of spousal grief after cancer and empirically derived ones converge toward the necessity to include the caregiving experience as determining grief reactions. A growing body of literature concerning prolonged grief disorders now provides integrative reflections regarding the characteristics of spousal loss, predictors, and associated therapeutic interventions in the cancer context. CONCLUSIONS: Few empirical studies (20 of 76) target spousal bereavement specifically after cancer. The process of adaptation to loss is usually decontextualized, removing any consideration of the relationship to the deceased or the experience of caregiving and dying. Our findings suggest that this topic warrants more studies that use both prospective and mixed methodologies, as well as explore typical grief needs and experiences of bereaved spouses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".