Examining the Conceptual and Measurement Overlap of Body Dissatisfaction and Internalized Weight Stigma in Predominantly Female Samples: A Meta-Analysis and Measurement Refinement Study
Bibliographic record
Abstract
Both body dissatisfaction and internalized weight stigma have been identified as risk factors for many negative health outcomes for women, including depression and eating disorders. In addition to these contributions, these concepts have been found to overlap to various degrees in existing literature. We conducted a systematic review and meta-analysis on articles published prior to February 2022 to demonstrate the conceptual and measurement overlap between body dissatisfaction and internalized weight stigma as currently quantified. We identified 48 studies examining the interrelation between body dissatisfaction and internalized weight stigma in predominantly female samples. Stronger correlations between these two constructs, some bordering on multicollinearity, were prevalent in community samples compared to clinical samples and with some but not all the commonly used measures in the body image and weight stigma fields. Body mass index (BMI) moderated these relations such that individuals with higher self-reported BMI were more likely to report lower correlations between the constructs. This concept proliferation, stronger for individuals with lower BMIs and community samples, necessitates the need change how we conceptualize and measure body dissatisfaction and internalized weight stigma. To this end, we conducted study two to refine existing measures and lessen the degree of measurement overlap between internalized weight stigma and body dissatisfaction, particularly in community samples of women. We aimed to clarify the boundaries between these two concepts, ensuring measurement error is better accounted for. Female university students completed existing measures of body satisfaction and internalized weight stigma, which were analyzed using an exploratory followed by a confirmatory factor analysis. In our attempts to modify two existing measures of internalized weight stigma and body dissatisfaction, the majority of the internalized weight stigma items were retained. In contrast, most of the body dissatisfaction items either cross-loaded onto both factors or loaded on to the internalized weight stigma factor despite being intended for the body dissatisfaction factor, suggesting that the measurement issues identified in recent prior research may be due not only to the way we conceptualize and quantify weight stigma, but also the ways in which we quantify body dissatisfaction, across the existing corpus of body dissatisfaction scales.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".