Prevalence and characterization of food insecurity in a Canadian paediatric emergency department
Bibliographic record
Abstract
Objectives: Food insecurity (FI) is associated with a number of adverse child health outcomes and increased emergency department (ED) use. The COVID-19 pandemic exacerbated the financial hardship faced by many families. We sought to determine the prevalence of FI among children with ED visits, compare this to pre-pandemic rates, and describe associated risk factors. Methods: From September to December 2021, families presenting to a Canadian paediatric ED were asked to complete a survey screening for FI along with health and demographic information. Results were compared to data collected in 2012. Multivariable logistic regression was used to measure associations with FI. Results: In 2021, 26% (n = 173/665) of families identified as food insecure compared to 22.7% in 2012 (n = 146/644) a difference of 3.3% (95% CI [-1.4%, 8.1%]). In multivariable analysis, greater number of children in the home (OR 1.19, 95% CI [1.01, 1.41]), financial strain from medical expenses (OR 5.31, 95% CI [3.45, 8.18]), and a lack of primary care access (OR 1.27, 95% CI [1.08, 1.51]) were independent predictors of FI. Less than half of families with FI reported use of food charity, most commonly food banks, while one-quarter received help from family or friends. Families experiencing FI expressed a preference for support through free or low-cost meals and financial assistance with medical expenses. Conclusion: More than one in four families attending a paediatric ED screened positive for FI. Future research is needed to examine the effect of support interventions for families assessed in medical care facilities including financial support for those with chronic medical conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".