Excessive Time on Social Networking Sites and Disordered Eating Behaviors Among Undergraduate Students: Appearance and Weight Esteem as Mediating Pathways
Bibliographic record
Abstract
Social networking sites (SNS) are a popular form of communication among undergraduate students. Body image concerns and disordered eating behaviors are also quite prevalent among this population. Maladaptive use of SNS has been associated with disordered eating behaviors; however, the mechanisms remain unclear. The present study examined if body image concerns (e.g., appearance and weight esteem) mediate the relationship between excessive time spent on SNS and disordered eating behaviors (restrained and emotional eating). The sample included 383 (70.2 percent female) undergraduate students (mean age = 23.08 years, standard deviation = 3.09) who completed self-report questionnaires related to SNS engagement, body image, disordered eating behaviors, and demographics. Parallel multiple mediation and moderated mediation analyses revealed that lower weight and appearance esteem mediated the relationship between excessive time on SNS and restrained eating for males and females, whereas appearance esteem mediated the relationship between excessive time on SNS and emotional eating for females only. The study adds to the literature by highlighting mediational pathways and gender differences. Intervention research is needed to determine if teaching undergraduate students more adaptive ways of using SNS or reducing exposure to SNS reduces body dissatisfaction and disordered eating in this high-risk population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".