Marital therapy for couples facing advanced cancer: Case review
Bibliographic record
Abstract
OBJECTIVE: The purpose of this article is to provide a brief review of the empirical literature regarding the impact of advanced cancer on the marital relationship. The link between attachment, caregiving, and care-receiving behaviors are defined. Both are activated and challenged in this population because the continuity of the marital bond is threatened, as well as the balance of reciprocal caregiving, often resulting in heightened marital distress. METHODS: Emotionally focused therapy (EFT), based in a synthesis of systemic, experiential, and attachment theory, is introduced as a marital protocol to both conceptualize and potentially mitigate the level of increased marital distress, and to achieve reciprocal caregiving. RESULTS: Two case studies are presented and support the benefit of EFT for those couples facing end of life. SIGNIFICANCE OF RESULTS: The findings from these case reviews advance the literature and offer an empirically validated marital therapy for this population. Such a protocol that emphasizes attachment theory and the inherent link to caregiving and care receiving may serve as a powerful tool to both explain and alleviate marital distress for couples facing end of life. Working models of attachment can contribute significantly to our understanding of why individuals' distress and their experience of emotional support from spouse caregivers vary in the context of end-stage cancer.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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; 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".