Food insecurity in households of children receiving care at a paediatric obesity management clinic in Montreal: Overall prevalence and changes associated with the COVID-19 pandemic
Bibliographic record
Abstract
Objectives: Food insecurity and paediatric obesity are two major public health issues in Canada that may have been exacerbated by the COVID-19 pandemic. We assessed food insecurity and its correlates among households of children receiving care at a paediatric obesity management clinic in Montreal. We also assessed whether the prevalence of food insecurity among households of children who received care during the COVID-19 pandemic differed from those who received care before it. Methods: This is a retrospective, cross-sectional analysis of medical records of children (2 to 17 years) who received care at a paediatric obesity management clinic in Montreal (Maison de santé prévention - Approche 180 [MSP-180]). Children's household food security status was assessed using Health Canada's Household Food Security Survey Module. Results: Among the 253 children included in the study, 102 (40.3%) lived in households with moderate (n=89; 35.2%) or severe food insecurity (n=13; 5.1%). Food insecurity was more prevalent in households of children who were first- or second-generation immigrants compared with those who were third generation or more (48.3% versus 30.1%; P=0.03). Prevalence of food insecurity among households of children who received care during the COVID-19 pandemic was 5.5% higher than among those who received care before the pandemic, but the difference was not statistically significant (39.6% versus 45.1%; P=0.65). Conclusions: Forty per cent of children treated at this paediatric obesity clinic lived in a food insecure household. This prevalence may have increased during the first year of the COVID-19 pandemic, but statistical power was insufficient to confirm it.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".