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Record W2888705885 · doi:10.1002/ejsp.2536

Why is it so hard to change? The role of self‐integrity threat and affirmation in weight loss

2018· article· en· W2888705885 on OpenAlexafffund
Christine Logel, William M. Hall, Elizabeth Page‐Gould, Geoffrey L. Cohen

Bibliographic record

VenueEuropean Journal of Social Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of TorontoUniversity of Waterloo
FundersCanadian Institutes of Health ResearchStanford University
KeywordsPsychologyWeight lossMaladaptive copingCoping (psychology)Weight changeExploratory researchSocial psychologyStructural integrityClinical psychologyDevelopmental psychologyObesityMedicine

Abstract

fetched live from OpenAlex

Abstract People are frequently dissatisfied with their body weight. Messages alleging that lower weight is esthetically preferable, healthier, and achievable likely trigger chronic self‐integrity threat, the sense that one's personal adequacy is in doubt. We examined whether self‐integrity threat, which creates stress and pressure to restore self‐integrity, contributes to the challenges of weight and behavior change. Weight‐dissatisfied women completed in‐lab tasks including a values affirmation manipulation and two‐month follow‐up. Affirmed women lost weight relative to controls, replicating previous research. Effects were primarily among those with higher initial body masses. Affirmed higher‐weighted women also ate more healthful foods compared to unhealthful foods in self‐reports and observation. Affirmed participants reported increased exercise, and an exploratory measure showed that their cortisol awakening responses synchronized with their coping needs, suggesting more adaptive physiological function. Results suggest that self‐integrity threat is an under‐recognized barrier to change, and reducing it can support healthy changes.

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 score0.516
Threshold uncertainty score0.792

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 head0.052
GPT teacher head0.359
Teacher spread0.308 · 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

Citations7
Published2018
Admission routes2
Has abstractyes

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