Exploring food and healthy eating with newcomers in Winnipeg’s North End
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the challenges and opportunities associated with attempting to maintain a healthy traditional diet for newcomers living in the North End neighbourhood of Winnipeg, Canada. Design/methodology/approach In this mixed-methods photovoice study the researcher used food photographs taken by participants to facilitate in-depth, semi-structured interviews with newcomers living in the area of interest. Community workers involved in food and newcomer programming were also interviewed. Qualitative data from the interviews were analysed using thematic analysis. Food security status of newcomer participants was also determined using The Household Food Security Survey Module. Findings Newcomer participants reported many struggles, including low incomes, gardening challenges and little access to culturally acceptable foods. Community worker interviews, field notes and an environmental scan of community resources also revealed a lack of social inclusion/support and few food and nutrition resources for newcomers. Originality/value Newcomers to Canada experience high rates of food insecurity and diminishing health status associated with length of time in Canada. This study demonstrates an imminent need for additional resources and programmes in this inner city community in order to decrease food security rates and help newcomers in Winnipeg to eat healthy traditional diets and avoid a decline in health.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".