Giving voice to food insecurity in a remote indigenous community in subarctic Ontario, Canada: traditional ways, ways to cope, ways forward
Bibliographic record
Abstract
BACKGROUND: Food insecurity is a serious public health issue for Aboriginal people (First Nations [FN], Métis, and Inuit) living in Canada. Food security challenges faced by FN people are unique, especially for those living in remote and isolated communities. Conceptualizations of food insecurity by FN people are poorly understood. The purpose of this study was to explore the perceptions of food insecurity by FN adults living in a remote, on-reserve community in northern Ontario known to have a high prevalence of moderate to severe food insecurity. METHODS: A trained community research assistant conducted semi-directed interviews, and one adult from each household in the community was invited to participate. Questions addressed traditional food, coping strategies, and suggestions to improve community food security and were informed by the literature and a community advisory committee. Thematic data analyses were carried out and followed an inductive, data-driven approach. RESULTS: Fifty-one individuals participated, representing 67% of eligible households. The thematic analysis revealed that food sharing, especially with family, was regarded as one of the most significant ways to adapt to food shortages. The majority of participants reported consuming traditional food (wild meats) and suggested that hunting, preserving and storing traditional food has remained very important. However, numerous barriers to traditional food acquisition were mentioned. Other coping strategies included dietary change, rationing and changing food purchasing patterns. In order to improve access to healthy foods, improving income and food affordability, building community capacity and engagement, and community-level initiatives were suggested. CONCLUSIONS: Findings point to the continued importance of traditional food acquisition and food sharing, as well as community solutions for food systems change. These data highlight that traditional and store-bought food are both part of the strategies and solutions participants suggested for coping with food insecurity. Public health policies to improve food security for FN populations are urgently needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".