Prevalence and severity of household food insecurity of First Nations people living in an on-reserve, sub-Arctic community within the Mushkegowuk Territory
Bibliographic record
Abstract
OBJECTIVE: To measure and describe the prevalence and severity of household food insecurity in a remote on-reserve First Nations community using the Household Food Security Survey Module (HFSSM) and to evaluate the perceived relevance of the HFSSM for this population. DESIGN: Household food security status was determined from the eighteen-item HFSSM following the classifications developed by Health Canada for the Canadian Community Health Survey, Cycle 2·2 Nutrition. One adult from each household in the community was invited to complete the HFSSM and to comment on its relevance as a tool to measure food security for First Nations communities. SETTING: Sub-Arctic Ontario, Canada. SUBJECTS: Households (n 64). RESULTS: Seventy per cent of households were food insecure, 17% severely and 53% moderately. The prevalence of food insecurity in households with children was 76%. Among respondents from homes rated as having severe food insecurity, all (100 %) reported worrying that food would run out, times when food didn't last and there wasn't money to buy more, and times when they couldn't afford to eat balanced meals. The majority of respondents felt the HFSSM did not capture an accurate picture of food security for their situation. Aspects missing from the HFSSM included the high cost of market food and the incorporation of traditional food practices. CONCLUSIONS: A high prevalence of household food insecurity was reported in this community. On-reserve remote First Nations communities may be more susceptible to food insecurity than off-reserve Aboriginal populations. Initiatives that promote food security for this vulnerable population are 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".