Examining Multiple Parenting Behaviors on Young Children’s Dietary Fat Consumption
Bibliographic record
Abstract
OBJECTIVE: To understand the association between parenting and children's dietary fat consumption, this study tested a comprehensive model of parenting that included parent household rules, parent modeling of rules, parent mediated behaviors, and parent support. DESIGN: Cross-sectional. SETTING: Baseline data from the MOVE/me Muevo project, a recreation site-based obesity prevention and control intervention trial. PARTICIPANTS: Five hundred forty-one parents of children between the ages of 5 and 8 years and living in San Diego County. MAIN OUTCOME MEASURE: Children's fat consumption based on parent report using a short food frequency questionnaire. ANALYSIS: A hierarchical linear regression was conducted. In exploratory analyses, a stepwise backward elimination approach was used. RESULTS: Children's fat consumption was positively associated with parent household rules (P < .01) and negatively associated with parent modeling of rules (P < .01). CONCLUSIONS AND IMPLICATIONS: Controlling parenting behaviors, such as rule setting, are associated with more frequent fat consumption, whereas role modeling healthful behaviors is associated with less frequent fat consumption. Changing parenting behaviors with regard to how they feed their children is a logical avenue for improving eating behaviors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".