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Record W2738917986 · doi:10.17975/sfj-2017-009

Weighing Oneself on a Scale Inhibits the Food Intake Enhancing Effect of Food Primes

2017· article· en· W2738917986 on OpenAlexfundvenueno aff
Elena Gupta, Catherine Wang, Rachel Corona, David A. Levitsky

Bibliographic record

VenueSTEM Fellowship Journal · 2017
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsnot available
FundersMcGill University
KeywordsSnack foodPsychologyObesityConsumption (sociology)Scale (ratio)Food intakeAdvertisingFood consumptionFood scienceEnvironmental healthSocial psychologyMedicineBusinessEndocrinologyBiologyGeography

Abstract

fetched live from OpenAlex

Frequent self-weighing has been found to facilitate weight maintenance and the prevention of age-related weight gain. Three experiments were performed to examine the hypothesis that the act of self-weighing inhibits the increase in food intake elicited by food primes. In the first study, participants were provided with a small bowl of chocolate, and consumption of the snack was compared between those who self-weighed and those who did not. In the second study, participants were randomized into three groups: (a) those who watched food advertisements and did not self-weigh, (b) those who watched food advertisements and self-weighed, and (c) those who watched neutral advertisements and did not self-weigh. The third study added a negative control group that watched neutral advertisements and self-weighed. The results revealed that weighing oneself inhibits snack intake, as well as reduces the effects of stimulating food intake when watching a food advertisement. Understanding the mechanism behind this phenomenon means that we can harness the psychological power of stepping on the scale to help combat the prevalence of obesity in the United States.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.059
GPT teacher head0.375
Teacher spread0.316 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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