Income precarity and child and parent weight change during the COVID-19 pandemic: a cross-sectional analysis of the Ontario Parent Survey
Bibliographic record
Abstract
OBJECTIVES: To describe child and parent weight change during the pandemic, overall and by income precarity. DESIGN: A cross-sectional online survey was conducted. SETTING: Caregivers of children 0-17 years of age living in Ontario, Canada, during the COVID-19 pandemic from May 2021 to July 2021. PARTICIPANTS: A convenience sample of parents (n=9099) with children (n=9667) living in Ontario were identified through crowdsourcing. PRIMARY OUTCOME MEASURE: Parents recalled, for themselves and their child, whether they lost weight, gained weight or remained the same over the past year. OR and 95% CI were estimated using multinomial logistic regression for the association between income precarity variables and weight loss or gain, adjusted for age, gender and ethnicity. RESULTS: Overall, 5.5% of children lost weight and 20.2% gained weight. Among adolescents, 11.1% lost weight and 27.1% gained weight. For parents, 17.1% reported weight loss and 57.7% reported weight gain. Parent weight change was strongly associated with child weight change. Income precarity measures, including job loss by both parents (OR=7.81, 95% CI 5.16 to 11.83) and disruption to household food supply (OR=6.05, 95% CI 4.77 to 7.68), were strongly associated with child weight loss. Similarly, job loss by both parents (OR=2.03, 95% CI 1.37 to 3.03) and disruption to household food supply (OR=2.99, 95% CI 2.52 to 3.54) were associated with child weight gain. CONCLUSIONS: Weight changes during the COVID-19 pandemic were widespread and income precarity was strongly associated with weight loss and weight gain in children and parents. Further research is needed to investigate the health outcomes related to weight change during the pandemic, especially for youth, and the impacts of income precarity.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".