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Record W2096272282

Child hunger in Canada: results of the 1994 National Longitudinal Survey of Children and Youth.

2000· article· en· W2096272282 on OpenAlexaffabout
Lynn McIntyre, Sarah K. Connor, James P. Warren

Bibliographic record

VenuePubMed · 2000
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsEmployment and Social Development CanadaDalhousie University
Fundersnot available
KeywordsCoping (psychology)Logistic regressionMedicineEnvironmental healthLongitudinal studyDemographyPsychologyGerontologyPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.218

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.146
GPT teacher head0.347
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations138
Published2000
Admission routes2
Has abstractyes

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