Food access, mobility, and transportation: a survey and key informant interviews of users of non-profit food hubs in the City of Vancouver before and during the COVID-19 crisis
Bibliographic record
Abstract
BACKGROUND: In the City of Vancouver, Canada, non-profit food hubs such as food banks, neighbourhood houses, community centres, and soup kitchens serve communities that face food insecurity. Food that is available yet inaccessible cannot ensure urban food security. This study seeks to highlight food access challenges, especially in terms of mobility and transportation, faced by users of non-profit food hubs in the City of Vancouver before and during the COVID-19 crisis. METHODS: This study involved an online survey (n = 84) and semi-structured follow-up key informant interviews (n = 10) with individuals at least 19 years old who accessed food at a non-profit food hub located in the City of Vancouver more than once before and during the COVID-19 crisis. RESULTS: 88.5% of survey respondents found food obtained from non-profit food hubs to be either very or somewhat important to their household's overall diet. In their journey to access food at non-profit food hubs in the City of Vancouver, many survey respondents face barriers such as transportation distance/time, transportation inconveniences/reliability/accessibility, transportation costs, line-ups at non-profit food hubs, and schedules of non-profit food hubs. Comments from interview participants corroborate these barriers. CONCLUSIONS: Drawing from the findings, this study recommends that non-profit food hubs maintain a food delivery option and that the local transportation authority provides convenient and reliable paratransit service. Furthermore, this study recommends that the provincial government considers subsidizing transit passes for low-income households, that the provincial and/or federal governments consider bolstering existing government assistance programs, and that the federal government considers implementing a universal basic income. This study emphasizes how the current two-tier food system perpetuates stigma and harms the well-being of marginalized populations in the City of Vancouver in their journey to obtain food.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".