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Record W2992719585 · doi:10.1097/spc.0000000000000476

Online couple interventions in cancer

2019· review· en· W2992719585 on OpenAlexaff
Ruth Vanstone, Karen Fergus

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2019
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreYork University
Fundersnot available
KeywordsMedicinePsychological interventionCancerMEDLINENursingInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Cancer diagnosis and treatment can have long-lasting psychological and physical consequences that affect both patients and their intimate partners. Improved understanding of extant dyadic interventions in the context of cancer, and how access to these may be enhanced through web-based technologies, introduce new directions for how cancer-related psychological distress for couples may be ameliorated. RECENT FINDINGS: Couples are negatively impacted by cancer, both individually, and as a dyad. Bolstering techniques to support effective communication about common cancer-related concerns and support for adjusting to new roles and responsibilities may help to strengthen the couple's relationship so partners are better able to cope with cancer. Although there are various intervention options available for couples dealing with cancer, many pose barriers to participation because of constraints on time and/or distance. However, online interventions have been shown to be effective, both in easing psychological distress and reducing participant burden. SUMMARY: Couples dealing with cancer experience psychological distress and must learn to navigate changing roles and responsibilities in the face of the disease. Online interventions offer flexible and innovative platforms and programs that help to address couples' educational needs while strengthening dyadic coping.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.822
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.368
GPT teacher head0.527
Teacher spread0.159 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations26
Published2019
Admission routes1
Has abstractyes

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