Long‐term effects of a healthy eating blog in mothers and children
Bibliographic record
Abstract
In the context of low consumption of vegetables and fruits and milk and alternatives among Canadian mothers and children, novel strategies are needed to improve maternal and child nutrition. This study evaluated the long-term effects of an evidence-informed healthy eating blog on dietary intakes and food-related behaviours of mothers and their child. The study presents a secondary outcome analysis of a randomised controlled trial in which 84 mothers (mean age of 37.6 ± 6.7 years) of 2- to 12-year-old children living in Quebec City, Canada, were randomly assigned to a dietary intervention delivered through a healthy eating blog written by a registered dietitian (RD; n = 42) or a control group (n = 42) during a period of 6 months. Dietary intakes, maternal eating behaviours, food parenting practices, and body weight were measured at baseline, 3 months, at the end of the intervention (6 months), and 6-month post-intervention (12 months). Differences between groups were assessed with mixed linear models. Globally, this study found no evidence of long-term differences in mean dietary intakes in mothers exposed to the blog and their children as well as other food-related outcomes and body weight compared with the control condition. Potential predictors of adherence to dietary recommendations in mothers and children (e.g., involvement of children in household food activities) were identified. In conclusion, a healthy eating blog written by an RD did not result in evidence of any long-term differences in dietary intakes and food-related behaviours in mothers and their children compared with the control condition.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".