Navigating Newcomers’ Food Transitions in the COVID-19 Pandemic: A developmental evaluation of a community-based program
Bibliographic record
Abstract
Abstract Refugee newcomers almost invariably face “food” culture shock and are at greater risk of food insecurity due to slow-to-evolve institutions. Community programs can help boost refugee newcomer confidence through knowledge exchange during intercultural culinary experiences. The originally proposed program was “Breaking Down the Walls (BDTW) - Building Integration and Cultural Appreciation through Shared Food Experiences with Refugee Newcomers”. With the restrictions set in place due to the COVID-19 pandemic, adaptations were made to the BDTW program. Specifically, the program’s scope was greatly reduced and the program itself shifted to a virtual environment. The final deliverables for this program included: 1) a framework/guide for conducting intercultural cooking events; and 2) an Online cultural brokerage training tool to help users to grasp some of the food-related challenges faced by newcomers to Canada. To best identify the challenges, successes, and efficacy of conducting community-based research, the team adopted a Developmental Evaluation approach, which is often used in complex settings and evolving scenarios such as the COVID-19 pandemic. The themes which emerged from interviews with participants were further distilled into broader areas of the COVID-19 pandemic, collaboration, and equity. Programs like BDTW have the potential to create infrastructure for newcomer nutrition programming that is integrated and streamlined as a long-term intervention. This type of programming would help shift care practices from sporadically addressing health/nutrition and settlement issues as they arise to a system that proactively anticipates nutritional needs from day one, ultimately promoting long-term health and mental wellbeing among newcomer 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 | 0.023 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".