What is known about the prevalence of household food insecurity in Canada during the COVID-19 pandemic: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Household food insecurity (HFI) is a persistent public health issue in Canada that may have disproportionately affected certain subgroups of the population during the COVID-19 pandemic. The purpose of this systematic review is to report on the prevalence of HFI in the Canadian general population and in subpopulations after the declaration of the COVID-19 pandemic in March 2020. METHODS: Sixteen databases were searched from 1 March 2020 to 5 May 2021. Abstract and full-text screening was conducted by one reviewer and the inclusions verified by a second reviewer. Only studies that reported on the prevalence of HFI in Canadian households were included. Data extraction, risk of bias and certainty of the evidence assessments were conducted by two reviewers. RESULTS: Of 8986 studies identified in the search, four studies, three of which collected data in April and May 2020, were included. The evidence concerning the prevalence of HFI during the COVID-19 pandemic is very uncertain. The prevalence of HFI (marginal to severe) ranged from 14% to 17% in the general population. Working-age populations aged 18 to 44 years had higher HFI (range: 18%-23%) than adults aged 60+ years (5%-11%). Some of the highest HFI prevalence was observed among households with children (range: 19%-22%), those who had lost their jobs or stopped working due to COVID-19 (24%-39%) and those with job insecurity (26%). CONCLUSION: The evidence suggests that the COVID-19 pandemic may have slightly increased total household food insecurity in Canada during the COVID-19 pandemic, especially in populations that were already vulnerable to HFI. There is a need to continue to monitor HFI in Canada.
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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 | 0.015 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".