Threat vigilance and socioeconomic disparities in metabolic health
Bibliographic record
Abstract
A quarter of the global population meets diagnostic criteria for metabolic syndrome (MetS). MetS prevalence stratifies by socioeconomic status (SES), such that low SES is associated with higher MetS risk starting in childhood. Despite this trend, some low-SES children maintain good metabolic health across the life span, but the factors responsible for their resilience are not well understood. This study examined the role of threat vigilance as either a moderator or a mediator of the effects of low early life SES on adult metabolic risk. Three hundred twenty-five Canadians aged 15-55 participated (M = 36.4 years, SD = 10.7; 55.4% female). We coded parental occupational status between the ages of 0 and 5 to index early life SES. We used the International Diabetes Federation case definition for MetS based on waist circumference, blood pressure, triglyceride levels, HDL cholesterol, and glycosylated hemoglobin measures. Threat vigilance was assessed using the Weapons Identification Procedure, a visual discrimination paradigm that captures implicit perceptions of threat. Analyses supported the moderator hypothesis: low early life SES was associated with MetS diagnosis exclusively among those with high levels of threat vigilance. This suggests that low early life SES environments that heighten vigilance to threat might be particularly detrimental for metabolic health. Conversely, low threat vigilance may buffer against the metabolic risks associated with socioeconomic disadvantage.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".