Body weight, perceived weight stigma and mental health among women at the intersection of race/ethnicity and socioeconomic status: insights from the modified labelling approach
Bibliographic record
Abstract
With increasing rates of obesity in the United States, attention to life chances and psychological consequences associated with weight stigma and weight-based discrimination has also intensified. While research has demonstrated the negative effects of weight-based discrimination on mental health, little is known about whether different social groups are disproportionately vulnerable to these experiences. Drawing on the modified labelling theory, the focus of this paper is to investigate the psychological correlates of body weight and self-perceived weight-based discrimination among American women at the intersection of race/ethnicity and socioeconomic status (SES). Analyses use data from the National Health Measurement Study (NHMS), a national multi-stage probability sample of non-institutional, English-speaking adults, ages 35 to 89 in 2005-2006. Our findings demonstrate that the effect of weight-based discrimination on psychological well-being is highly contingent on social status. Specifically, the psychological consequences of discrimination on Hispanic women and women in the lowest household income group is significantly greater relative to White women and women with higher household income, controlling for obesity status and self-rated health. These results suggest that higher social status has a buffering effect of weight stigma on psychological well-being.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".