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Record W3041265501 · doi:10.1002/eat.23336

Understanding “feeling fat” and its underlying mechanisms: The importance of multimethod measurement

2020· article· en· W3041265501 on OpenAlexafffund
Adrienne Mehak, Sarah E. Racine

Bibliographic record

VenueInternational Journal of Eating Disorders · 2020
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsFeelingPsychologyCognitionSocial psychologyDevelopmental psychologyCognitive psychologyClinical psychologyNeuroscience

Abstract

fetched live from OpenAlex

"Feeling fat," the somatic sensation of being overweight that does not entirely correlate with one's actual weight, is recognized clinically as a maintenance factor in eating disorders. Occurring amidst Western internalized thin ideals and weight stigma, "feeling fat" is wide-reaching and also reported by those with subclinical and absent eating pathology. However, empirical study of "feeling fat" is limited. Regarding proposed mechanisms, "feeling fat" may (a) reflect the displacement of negative affect onto the body, (b) represent one element of a cognitive distortion related to the imagined consumption of fattening food, and/or (c) be a function of impaired interoceptive awareness. However, the relative and/or joint contributions of these mechanisms to "feeling fat" are unclear. Regarding measurement, retrospective assessment with single items has been the norm. Innovative measures of "feeling fat" will expand our understanding of this construct. Ecological momentary assessment can clarify the transitory nature of this experience, and physiological measures can allow for assessment of somatic elements of "feeling fat. Multi-method and implicit measurement strategies of 'feeling fat'" may clarify the mechanisms underlying this experience. Further research with improved measurement techniques may also benefit eating disorder treatment by highlighting new treatment foci (e.g., cognitive distortions, interoceptive awareness).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score0.428

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)0.0000.000

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 head0.245
GPT teacher head0.386
Teacher spread0.141 · 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

Citations32
Published2020
Admission routes2
Has abstractyes

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