Black-white Racial Disparities in Household Food Insecurity from 2005-2014, Canada
Bibliographic record
Abstract
Abstract Background: Household food insecurity is a public health concern in many high-income countries. Despite two decades of research charting the socio-demographic and geographic correlates of food insecurity in Canada, the relationship between race and vulnerability to food insecurity has not been interrogated. Our objectives were to examine the association of Black-White racial identity and prevalence and severity of household food insecurity in Canada, and understand how racialized vulnerability manifests differently for key sociodemographic predictors. Methods: Data for households with Black and White respondents with complete data on household food insecurity were drawn from the Canadian Community Health Survey cycles from 2005-2014 (N=491,400). Household food insecurity status was assessed using the Household Food Security Survey Module. Bivariate and multivariate logistic and multinomial regression models were run, including respondent’s race and immigration status, as well as six well-established household-level predictors of food insecurity in the general population: household composition, income, housing tenure, highest level of education in the household, main household income source, and province/territory. To test whether the relationship between food insecurity and these predictors differed by race, additional multivariable logistic regression models were run, with race interacted with each predictor individually and predicted probabilities estimated.Results: The weighted prevalence of household food insecurity was 10.0% for white respondents and 28.4% for Black respondents. The odds of Black households being food-insecure compared to white households fell from 3.56 (95% CI: 3.30-3.85), to 1.88 (95% CI: 1.70-2.08) with adjustment for household sociodemographic characteristics. In contrast to white households, there was relative homogeneity of risk of food insecurity among Black sub-groups defined by immigration status, household composition, education, and province of residence. Homeownership was associated with lower probabilities of food insecurity for Black and white households, but the probability among Black owners was similar to that for white renters (14.7% vs. 14.3%). Black households had significantly higher predicted probabilities of food insecurity than their white counterparts across all main sources of household income except child benefits and social assistance.Conclusions: Being racialized as Black appears to be an overriding factor predicting vulnerability to food insecurity for the Black population in Canada.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".