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Record W2111602224 · doi:10.1017/s1478951507000466

Marital therapy for couples facing advanced cancer: Case review

2007· article· en· W2111602224 on OpenAlexaff
Linda M. McLean, Rinat Nissim

Bibliographic record

VenuePalliative & Supportive Care · 2007
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsPrincess Margaret Cancer CentreYork UniversityUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMarital TherapyCancer therapyCancerMedicinePsychotherapistPsychologyInternal medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.388
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations34
Published2007
Admission routes1
Has abstractyes

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