COVID-19: First wave impacts on the Charitable Food Sector in Manitoba, Canada
Bibliographic record
Abstract
The first wave of the COVID-19 pandemic led to significant socioeconomic changes in Canada due to business and school closures, and related job losses. This increased food insecurity among vulnerable populations, as well as many who had not been previously food insecure, placing unprecedented demand on charitable food organizations. This study documented the pandemic’s impact on charitable food organizations in Manitoba, Canada during the first wave in spring 2020. Using a multi-method design, data on pandemic-related program challenges and newly implemented policies/procedures were collected from: food bank organization websites and Facebook pages; online news media outlets; and semi-structured interviews with food organization leadership. Inductive thematic analysis was used to identify emerging patterns and themes. Second level coding was used to integrate data from different sources. Six challenge themes emerged: increased need for services; acquisition and distribution of food supply; staff and volunteer resource management; emotional vulnerability of staff, volunteers, and clients; difficulties with internal and external communications; and lack of structural supports. Five policy/procedure themes emerged: program and service delivery changes; finance and administrative changes; safety protocols; advocacy for resources and community engagement; and changes to paid and volunteer staffing. The first wave of COVID-19 had a significant impact on the Manitoba charitable food sector. Food banks re-configured programs to meet client needs amid shifting public health directives, with diminished resources, rising demand, and insufficient government support. Despite the resiliency of community food organizations during the pandemic, the status quo with respect to addressing food insecurity is inefficient and inadequate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".