Screen use while eating is associated with lower intuitive eating and higher disordered eating in <scp>Chinese</scp> adult men and women
Bibliographic record
Abstract
OBJECTIVE: We aimed to explore the potential associations between screen use while eating and intuitive eating and disordered (thinness-oriented and muscularity-oriented) eating behaviors. METHODS: = 30.67, SD = 8.08) recruited online. The use of four major screen devices was captured, including cell phones, tablets, computers, and TVs. Three types of eating behaviors were examined, namely intuitive eating, thinness-oriented disordered eating, and muscularity-oriented disordered eating. Pearson correlation and hierarchical regression analyses were conducted separately for men and women to examine the sex-specific associations between screen use while eating and intuitive eating, thinness-oriented disordered eating, and muscularity-oriented disordered eating. RESULTS: In both men and women, more screen use while eating was significantly associated with lower intuitive eating, higher thinness-oriented disordered eating, and higher muscularity-oriented disordered eating, above and beyond total screen time and social media use. DISCUSSION: We found preliminary evidence for the significant links between screen use while eating and intuitive eating and disordered eating. Given the global increases in screen use, continued research is warranted to further explore the role of screen use while eating in the development and maintenance of intuitive eating and disordered eating. PUBLIC SIGNIFICANCE: Much remains unknown regarding screen use while eating and eating behaviors. We found that in both Chinese men and women, more screen use while eating was significantly and uniquely associated with lower intuitive eating, higher thinness-oriented disordered eating, and higher muscularity-oriented disordered eating. Findings highlight the importance of incorporating screen use while eating in future research on intuitive eating and disordered eating.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".