Household Crowding and Food Insecurity Among Inuit Families With School-Aged Children in the Canadian Arctic
Bibliographic record
Abstract
OBJECTIVES: We examined the relation of household crowding to food insecurity among Inuit families with school-aged children in Arctic Quebec. METHODS: We analyzed data collected between October 2005 and February 2010 from 292 primary caregiver-child dyads from 14 Inuit communities. We collected information about household conditions, food security, and family socioeconomic characteristics by interviews. We used logistic regression models to examine the association between household crowding and food insecurity. RESULTS: Nearly 62% of Inuit families in the Canadian Arctic resided in more crowded households, placing them at risk for food insecurity. About 27% of the families reported reducing the size of their children's meals because of lack of money. The likelihood of reducing the size of children's meals was greater in crowded households (odds ratio=3.73; 95% confidence interval=1.96, 7.12). After we adjusted for different socioeconomic characteristics, results remained statistically significant. CONCLUSIONS: Interventions operating across different levels (community, regional, national) are needed to ensure food security in the region. Targeting families living in crowded conditions as part of social and public health policies aiming to reduce food insecurity in the Arctic could be beneficial.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".