Eating Problems Among Adolescent Boys and Girls Before and During the Covid‐19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: Studies suggest that adolescents reported more eating problems during the pandemic. Using a population-based sample, we compared eating problems-and how they associate with a range of personal characteristics and genetic factors-among adolescents before (June 2017-April 2020) versus during (April 2020-December 2022) the pandemic. METHOD: Based on a preregistered analysis plan, we used cross-sectional data collected from 22,706 14-16-year-olds over 6 years (55% during the pandemic) in the Norwegian Mother, Father, and Child Cohort. We used measurement invariance analyses to compare the level of eating restraint and body concern before and during the pandemic, and multi-group structural equation models to estimate pre-pandemic and pandemic patterns of associations. RESULTS: Pandemic responders generally reported more eating problems than pre-pandemic responders, specifically on dieting and body dissatisfaction. However, after adjusting for a general linear increase in eating problems across all 6 years of data collection, the pandemic itself seems to be associated with more eating problems only among girls, reporting more eating restraints (meanΔ = 0.14 [CI: 0.07, 0.20]) and body concern (meanΔ = 0.17 [CI: 0.11, 0.23]). Associations between eating problems and a range of other characteristics did not differ across the pandemic and pre-pandemic groups. CONCLUSIONS: There was a general increase in eating problems among 14-16-year-olds over time. Adjusting for this trend, the pandemic seems to exacerbate problems among girls. Although the mechanisms are unclear, our results point to factors susceptible to change that could have been intensified during the pandemic (e.g., screen time, mental distress). Our results highlight the importance of recognizing sex-specific differences in eating problems.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".