Assessing the relevance of neighbourhood characteristics to the household food security of low-income Toronto families
Bibliographic record
Abstract
OBJECTIVE: Although the sociodemographic characteristics of food-insecure households have been well documented, there has been little examination of neighbourhood characteristics in relation to this problem. In the present study we examined the association between household food security and neighbourhood features including geographic food access and perceived neighbourhood social capital. DESIGN: Cross-sectional survey and mapping of discount supermarkets and community food programmes. SETTING: Twelve high-poverty neighbourhoods in Toronto, Ontario, Canada. SUBJECTS: Respondents from 484 low-income families who had children and who lived in rental accommodations. RESULTS: Food insecurity was pervasive, affecting two-thirds of families with about a quarter categorized as severely food insecure, indicative of food deprivation. Food insecurity was associated with household factors including income and income source. However, food security did not appear to be mitigated by proximity to food retail or community food programmes, and high rates of food insecurity were observed in neighbourhoods with good geographic food access. While low perceived neighbourhood social capital was associated with higher odds of food insecurity, this effect did not persist once we accounted for household sociodemographic factors. CONCLUSIONS: Our findings raise questions about the extent to which neighbourhood-level interventions to improve factors such as food access or social cohesion can mitigate problems of food insecurity that are rooted in resource constraints. In contrast, the results reinforce the importance of household-level characteristics and highlight the need for interventions to address the financial constraints that underlie problems of food insecurity.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".