Parenting stress: a cross-sectional analysis of associations with childhood obesity, physical activity, and TV viewing
Bibliographic record
Abstract
BACKGROUND: Parents influence their children's obesity risk through feeding behaviours and modeling of weight-related behaviours. Little is known about how the general home environment, including parental stress, may influence children's weight. The objective of this study was to explore the association between parenting stress and child body mass index (BMI) as well as obesity risk factors, physical activity and television (TV) viewing. METHODS: We used cross-sectional data from 110 parent-child dyads participating in a community-based parenting intervention. Child heights and weights were measured by trained research assistants. Parents (93% mothers) reported level of parenting stress via the Parenting Stress Index- Short Form (PSI-3-SF) as well as children's activity behaviours and TV viewing. This was an ethnically diverse (55% Hispanic/Latino, 22% Black), low-income (64% earning < $45,000/year) sample. RESULTS: Level of parenting stress was not associated with children's risk of being overweight/obese. Children with highly stressed parents were less likely to meet physical activity guidelines on weekdays than children with normally stressed parents (OR = 0.33, 95% CI, 0.12-0.95). Parents experiencing high stress were less likely to set limits on the amount of TV their children watched (OR = 0.32, 95% CI, 0.11, 0.93). CONCLUSION: Results suggest stress specific to parenting may not be associated with increased obesity risk among children. However, future interventions may need to address stress as a possible underlying factor associated with unhealthful behaviours among preschoolers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".