Policy responses to the COVID-19 pandemic in the Manitoba grocery sector: a qualitative analysis of media, organizational communications, and key informant interviews
Bibliographic record
Abstract
OBJECTIVES: The COVID-19 pandemic has impacted all aspects of the food system, including the retail grocery sector. We sought to (objective 1) document and (objective 2) analyze the policies implemented in the grocery sector during the first wave of the pandemic in Manitoba, Canada. METHODS: Our qualitative policy analysis draws from organizational communications (websites and social media) (n = 79), news media articles (n = 95), and key informant interviews with individuals (n = 8) working within the grocery sector in urban and rural, Manitoba. Media and communications were extracted between March 9-May 8, 2020 and interviews were conducted in July-August, 2020. RESULTS: Newly implemented policies due to the pandemic fell under four inter-related themes: Employee health and wellbeing, Safety measures, Operational measures, and Community support. Employee health and wellbeing included sub-themes of financial and social support, health recommendations and protocols, and new employee guidelines. Safety measures encompassed numerous policies pertaining to sanitation, personal protection, transmission prevention, physical distancing, and limiting access. Overall, new policies were discussed as effective in making grocery shopping as safe as possible given the situation. Compliance and enforcement, employee teamwork, and support for employees were key themes related to perceptions of policy success in a challenging and inequitable context. Nevertheless, government support and communication was needed as well to ensure safety within the grocery sector. CONCLUSIONS: The grocery sector reacted to the pandemic with the swift implementation of policies to address food supply issues, prevent transmission of the virus, support their employees as essential workers, and better serve high-risk populations.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".