Resiliency against food insecurity among the Black population in Scarborough during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Background One of the effects of the COVID-19 pandemic is the increased level of food insecurity, especially during the first wave. Food insecurity is an indication of poverty and results in serious health and social effects. Even though several studies have been conducted to assess the impact of COVID-19, there is a paucity of information on the role of individual community members and local organizations in addressing food insecurity in the province of Ontario, Canada. Consequently, the objective of this study is to examine the role of individuals and community organizations in addressing food insecurity challenges among the Black population in Scarborough in the Greater Toronto Area. Methods This qualitative study recruited 20 Black participants from the TAIBU Community Health Center (CHC) located in Scarborough. Furthermore, the study recruited eight nurses and two Black doctors in the Greater Toronto Area (GTA) but only one affiliated with TAIBU. In-depth interviews were used to gather information for analysis. The study used manual coding and NVivo software to analyze the qualitative data. Results The study found that there was a reported incidence of food insecurity among the population but new local food aid organizations sprang up to assist the existing ones in tackling food insecurity. However, the study found that the operations of food aid organizations are not sustainable. Conclusions Despite the reported cases of food insecurity, local community organizations and individual community members volunteered to support people to boost their resiliency to food insecurity. The findings of the study highlight the role of community organizations in addressing food insecurity during crises including pandemics. Based on the health effects of food insecurity, the study recommends that both federal and provincial governments prioritize food insecurity as a major public health issue.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".