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Record W2168656214 · doi:10.1177/1359105310393543

Can’t Live with You, Can’t Live without You: Negative Family Exchanges and Adaptation in End-stage Renal Disease Patients

2011· article· en· W2168656214 on OpenAlexafffund
Georgia Pomaki, Anita DeLongis, Tanya Anagnostopoulou, Jayme Heininger

Bibliographic record

VenueJournal of Health Psychology · 2011
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaMichael Smith Health Research BC
KeywordsEnd stage renal diseaseFeelingDiseaseAffect (linguistics)MedicineIntervention (counseling)Adaptation (eye)PerceptionPsychologyClinical psychologyPsychiatryInternal medicineSocial psychology

Abstract

fetched live from OpenAlex

The present study examined associations between negative family exchanges (patient perceptions of family overprotection and negative expressiveness, and patient feelings of being a burden), and two indicators of adaptation to illness (mental health and negative affect) in patients with end-stage renal disease (ESRD). Data were collected using the perceptions of illness-related family exchanges questionnaire, the SF36 and the PANAS in a structured interview with 127 patients with ESRD in Greece. Findings indicate that patient perceptions of negative family exchanges are related to outcomes, even after controlling for demographic and clinical factors. It is important to consider caregivers in intervention programmes of ESRD patients.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.074
GPT teacher head0.350
Teacher spread0.276 · 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 designObservational
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

Citations7
Published2011
Admission routes2
Has abstractyes

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