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Record W1130298614 · doi:10.1007/s12160-015-9728-x

Predicting Daily Satisfaction with Spouse Responses Among People with Rheumatoid Arthritis

2015· article· en· W1130298614 on OpenAlexafffund
R. Thomas Beggs, Susan Holtzman, Anita DeLongis

Bibliographic record

VenueAnnals of Behavioral Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsUniversity of GuelphUniversity of British Columbia, Okanagan CampusOkanagan University CollegeUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsSpouseSocial supportAffect (linguistics)Rheumatoid arthritisClinical psychologyHealth psychologyPsychologyMedicinePublic healthSocial psychologyInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Social support has been strongly linked to health outcomes. However, the factors associated with satisfaction with social support remain poorly understood. PURPOSE: We examined the impact of different types of support, affect, marital satisfaction, personality, and disease-related variables on day-to-day and overall satisfaction with spouse responses. METHODS: Sixty-nine married people with rheumatoid arthritis completed an initial structured interview, followed by twice-daily phone interviews for 1 week. RESULTS: Higher levels of esteem support were associated with increased satisfaction, whereas negative spouse responses were related to decreased satisfaction across the day. Greater positive affect and lower pain were associated with higher concurrent satisfaction, but the effects did not last over the day. At the between-person level, older age and lower fatigue were related to higher satisfaction. CONCLUSIONS: Several key factors related to support satisfaction were identified. Esteem support appeared to play a particularly important role and warrants attention in future research.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.068
GPT teacher head0.345
Teacher spread0.277 · 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

Citations4
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueAnnals of Behavioral MedicineSame topicRheumatoid Arthritis Research and TherapiesFrench-language works237,207