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Record W4387009384 · doi:10.32920/24194706

Is Self-Compassion an Effective Strategy for Improving Body Image and Related Constructs among Women with Normal Weight and Excess Weight?

2023· preprint· en· W4387009384 on OpenAlexaff
Bethany Nightingale

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsToronto Metropolitan UniversityUniversity of Waterloo
Fundersnot available
KeywordsSelf-compassionInternalizationPsychologyBody weightPsychological interventionWeight stigmaClinical psychologyCompassionAffect (linguistics)Developmental psychologyBody mass indexMedicineInternal medicinePsychiatryMindfulnessOverweightTheology

Abstract

fetched live from OpenAlex

Negative attitudes towards one’s own body are common amongst women in Western society and have been linked to adverse consequences including negative affect, low self-esteem, and eating pathology. Self-compassion has been found effective in improving body image; however, no published studies have examined self-compassion in populations with excess weight despite the positive correlation between weight and body dissatisfaction. The current study examined the efficacy of a self-compassion exercise in participants with negative body image and either normal or excess weight to determine whether self-compassion differently affects body image, affect, and self-esteem across weight groups. The self-compassion exercise promoted more adaptive body image and self-compassion than the control condition for those with high weight bias internalization (WBI; i.e., internalization of society’s negative stigma against those with excess weight). Additionally, WBI was significantly related to less adaptive levels of outcome variables regardless of condition. Implications for future body image interventions are discussed.

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)0.001
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0030.000

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 head0.012
GPT teacher head0.291
Teacher spread0.280 · 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
Published2023
Admission routes1
Has abstractyes

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