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Record W1876827785 · doi:10.1300/j077v18n02_03

Partners of Cancer Patients

2000· article· en· W1876827785 on OpenAlexaff
Linda E. Carlson, Barry D. Bultz, Michael Speca, M Pierre

Bibliographic record

VenueJournal of Psychosocial Oncology · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsOntario Institute for Cancer Research
Fundersnot available
KeywordsPsychosocialPsychological interventionDistressCancerPsychologyMedicineClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Because the importance of partners' role in determining the adjustment of patients with cancer has become increasingly evident, partners have become the target of studies more extensively in recent years. It also is apparent that partners often suffer more psychological distress from the impact of the diagnosis than patients do. This two-part review identifies three areas of research that have not been integrated in the literature: (1) the impact of cancer on the partner across different stages of the illness trajectory, (2) gender differences in partners' reactions and adjustment, and (3) psychosocial interventions for partners. Part I focuses on the experience of cancer patients' partners, how the illness affects them, and how they adjust to and cope with the illness. Part II will review the interventions that have been developed to help patients' partners and conclude with suggestions for providing interventions specifically tailored for different populations of partners.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.031
GPT teacher head0.424
Teacher spread0.393 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations93
Published2000
Admission routes1
Has abstractyes

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