Attitudes Toward Submental Fat Among Adults in the United States
Bibliographic record
Abstract
BACKGROUND: Excess submental fat (SMF) can cause submental fullness resulting in negative perceptions of individuals. However, the impact of SMF on perceptions of social traits has not been well studied. OBJECTIVE: To characterize the impact of SMF on external value judgments in adults in the United States. METHODS: Respondents completed an online survey in which they reacted to statements about individuals with varying grades of SMF. Attributes were rated on a scale from 0 to 100 with higher scores for more positive attributes. RESULTS: Similar proportions of respondents (N = 1996) indicated that women and men with double chins were less attractive than those without (91% and 90%, respectively). A double chin was more likely to be noticed on a woman than on a man (78% of respondents). With increasing SMF, individuals were perceived as significantly less likeable, intelligent, happy, active, and easygoing. Those with greater amounts of SMF were rated as significantly less attractive than those with less SMF. For all attributes, male respondents rated all individuals lower than female respondents did. CONCLUSION: Results from this study provide further evidence of negative perceptions of individuals with SMF. Aesthetics of the submental area, especially SMF, likely impact the overall assessment of attractiveness and social attributes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".