COVID-19-Related distress, body image, and eating behaviors: a cross-sectional explanatory model
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has been associated with increased body dissatisfaction and disordered eating behaviors in adolescents. To better understand these associations, this study tested an explanatory model in which appearance-focused social media use, internalization of social media pressure, and mindfulness abilities mediated the relationship between COVID-related distress and body dissatisfaction, which in turn was associated with dietary restraint and binge eating episodes. METHODS: = 16.7; 47.5% girls) recruited within high schools completed online measures. We conducted model testing through path analysis in MPlus, using modification indices to derive a well-fitting model. RESULTS: The initially hypothesized model was a poor fit for the data. The final well-adjusted model confirmed several significant paths and supported the parallel mediational roles of social media (specifically, the use of appearance-focused social media and internalization of social media pressure) along with mindfulness, on the relationship between COVID-19-related distress and body satisfaction. Model adjustments involved adding three paths, resulting in two additional significant indirect effects, and suppressing one path. CONCLUSIONS: Findings suggest that mindfulness, media use and the internalization of social media pressure are potential key processes explaining body dissatisfaction and eating disorders among adolescents who experienced higher levels of COVID-related distress.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".