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Record W3136334693 · doi:10.33921/bayg3556

Dietary and marital profile by partner weight asymmetry

2017· article· en· W3136334693 on OpenAlexvenueno aff
Jessica Philippe, Marie-Ève Bergeron, Marilou Côté

Bibliographic record

VenueJournal of Interpersonal Relations Intergroup Relations and Identity · 2017
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsnot available
Fundersnot available
KeywordsOverweightPsychologyBody mass indexDevelopmental psychologyDisordered eatingNormal weightWeight lossDemographyObesityClinical psychologyMedicineEating disordersEndocrinology

Abstract

fetched live from OpenAlex

This study aimed to compare heterosexual mixed-weight (one overweight and one healthy weight partner) and matched-weight couples on their relationship functioning and eating behaviors. One hundred seventy- four adult couples were recruited and grouped based on their body mass index. They completed a survey online. It was expected that mixed-weight couples would report poorer marital satisfaction and more eating related problems than matched-weight couples, especially among couples with overweight women and healthy weight men. Results showed that men from mixed-weight couples were less satisfied of their relationship and their sexuality compared to men from matched-weight couples. Thus, a gap between partners’ weight seems to be associated with men’s dissatisfaction, no matter which partner is overweight. However, this weight asymmetry has no impact on women’s satisfaction. These findings provide an informative contribution to scientific literature on the impact of weight asymmetry on couple relationship.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.334
Teacher spread0.317 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Interpersonal Relations Intergroup Relations and IdentitySame topicEating Disorders and BehaviorsFrench-language works237,207