Fathers’ use of social media for social comparison is associated with their food parenting practices
Bibliographic record
Abstract
Over 85% of parents use social media; however, limited research has investigated the associations between parental social media use and food parenting practices. The objectives of this study were to: 1) describe how mothers and fathers use social media focused on topics related to child feeding and family meals; and 2) examine associations between parental social media use focused on child feeding and family meals and mothers' and fathers' food parenting practices. Data were obtained from 179 mothers and 116 fathers of children aged 3-8 years enrolled in a family-based obesity prevention intervention. We used descriptive statistics to describe parents' social media use in relation to child feeding and family meals and linear regressions with generalized estimating equations to explore associations between parents' social media use and food parenting practices. Models were stratified by parent gender and adjusted for household income, parent ethnicity, parent age, child sex, and intervention status. A higher percent of mothers than fathers reported using social media to seek information related to child feeding and family meals (64.8% mothers; 25.0% fathers) and to share and compare family meals and food choices (41.9% mothers; 19.8% fathers). While social media use was not associated with food parenting practices in mothers, fathers' social media use to share and compare family meals and food choices was associated with negative food parenting practices, i.e., greater use of food for emotional regulation (β = 0.37, p = 0.02) and greater use of food for reward (β = 0.34, p = 0.02). Study results can inform strategies to promote healthy social media use among parents of young children.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".