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Record W2824158136 · doi:10.1037/fsh0000362

The role of partner autonomy support in motivation, well-being, and weight loss among women with higher baseline BMI.

2018· article· en· W2824158136 on OpenAlexfundno aff
Katelyn Gettens, Noémie Carbonneau, Richard Koestner, Theodore A. Powers, Amy A. Gorin

Bibliographic record

VenueFamilies Systems & Health · 2018
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteFonds de Recherche du Québec-Société et Culture
KeywordsPsycINFOPsychologyBody mass indexAutonomyWeight lossOverweightSocial supportIntervention (counseling)Clinical psychologySelf-determination theoryLongitudinal studyDevelopmental psychologyObesityMedicineSocial psychologyMEDLINEPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: ). METHOD: In Study 1, autonomy support was measured as male partners' report of their behavior in a cross-sectional design. In Study 2, autonomy support was measured as female participants' perceptions of their partners' behavior in a longitudinal home environment-based behavioral weight loss intervention. RESULTS: Study 1 showed that autonomy support from partners was associated with greater self-determined motivation for healthy eating and self-reported well-being among women with higher BMI. Study 2 showed that changes in partner autonomy support over 18 months of a home-based weight loss intervention were associated with increases in motivation for treatment and greater weight loss, especially for women who had higher baseline BMI. DISCUSSION: Both studies demonstrated that autonomy support was associated with adaptive functioning across weight status but that it was especially potent for women with higher BMI. This pattern of findings is explained in terms of the pressures women with higher BMI may feel about their weight-related behaviors. (PsycINFO Database Record

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.386
Threshold uncertainty score0.775

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.264
Teacher spread0.254 · 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.

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

Citations16
Published2018
Admission routes1
Has abstractyes

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