Child hunger in Canada: results of the 1994 National Longitudinal Survey of Children and Youth.
Bibliographic record
Abstract
BACKGROUND: In Canada, hunger is believed to be rare. This study examined the prevalence of hunger among Canadian children and the characteristics of, and coping strategies used by, families with children experiencing hunger. METHODS: The data originated from the first wave of data collection for the National Longitudinal Survey of Children and Youth, conducted in 1994, which included 13,439 randomly selected Canadian families with children aged 11 years or less. The respondents were asked about the child's experience of hunger and consequent use of coping strategies. Sociodemographic and other risk factors for families experiencing hunger, use of food assistance programs and other coping strategies were analyzed by means of multiple logistic regression analysis. RESULTS: Hunger was experienced by 1.2% (206) of the families in the survey, representing 57,000 Canadian families. Single-parent families, families relying on social assistance and off-reserve Aboriginal families were overrepresented among those experiencing hunger. Hunger coexisted with the mother's poor health and activity limitation and poor child health. Parents offset the needs of their children by depriving themselves of food. INTERPRETATION: Physicians may wish to use these demographic characteristics to identify and assist families with children potentially at risk for hunger.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".