Measuring adolescents’ weight socioeconomic gradient using parental socioeconomic position
Bibliographic record
Abstract
Background: There is an evidence of social inequalities in weight status in adolescence but the diversity of family socioeconomic status (SES) indicators can lead to discrepant findings. We aimed to identify how combination of family SES indicators can help measuring weight socioeconomic gradient (WSG) among adolescents. Methods: Cross-sectional data from 2113 adolescents (13-18 years old) of the PRALIMAP-INÈS trial were used. Multiple SES indicators and assessment of weight status including body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR) and self-perception of overweight were used. We used principal component analysis (PCA) followed by structural equation models to identify SES dimensions. A dimension normalized score was calculated ranging from 1 to 10 (a high score corresponding to high SES). Linear regression models (linear trend test) were used to assess the WSG. Results: Three SES dimensions were identified: (i) 'Family social status', (ii) 'Family education level' and (iii) 'Family income level'. BMI was significantly lower in highly advantaged compared with highly less advantaged [-1.64 (-2.39; -0.89) for family social dimension, -0.86 (-1.37; -0.36) for family education level and -2.35 (-3.65; -1.05) for family income level]. Similar results were observed for all weight indicators excepted for self-perception of overweight status. Socially less advantaged adolescents perceived themselves less fat than they were. Conclusion: Although WSG was evident in adolescence, association between SES and weight status differed according to objective or perceived weight indicators. The proposed SES dimension can be applied in other field and future studies are needed to confirm our findings.
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 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".