Exploring the Implications of COVID-19 on Food Security and Coping Strategies among Urban Indigenous Peoples in Saskatchewan, Canada
Bibliographic record
Abstract
The COVID-19 pandemic has notably impacted food security, especially among urban Indigenous communities. This study aimed to examine the impact of the pandemic and related lockdown measures on the food security of urban Indigenous peoples in Saskatchewan, Canada. In partnership with Indigenous co-researchers, we designed an online survey disseminated via SurveyMonkey® (San Mateo, CA, USA) from August 2021 to August 2022. This survey detailed background information, the Household Food Security Survey Module (HFSSM), state of food access, and traditional food consumption habits. Of the 130 Indigenous respondents, 75.8% were female, 21.9% male, and 2.3% non-binary, with an average age of 36.2 years. A significant 68.4% experienced food insecurity during the pandemic’s first four months. Increased food prices (47.1%) and reduced market availability (41.4%) were the dominant causes. Additionally, 41.8% highlighted challenges in accessing traditional foods. Relying on community resources and government food distribution programs (40.7%) was the most reported coping strategy for those experiencing food insecurity. Notably, 43.6% reported receiving no government financial support during the crisis. This study emphasizes the severe food insecurity among urban Indigenous communities in Saskatchewan during the pandemic. The findings highlight the immediate need for interventions and policies that ensure access to culturally relevant food, especially for future crises.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".