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Record W2134191920 · doi:10.1093/jpepsy/jsu089

Province-Level Income Inequality and Health Outcomes in Canadian Adolescents

2014· article· en· W2134191920 on OpenAlexfundaboutno aff
Elizabeth Quon, Jennifer J. McGrath

Bibliographic record

VenueJournal of Pediatric Psychology · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsSocioeconomic statusMental healthInequalityHealth equityEconomic inequalityHousehold incomeFamily incomePsychologySocial inequalityLimitingSocial classEnvironmental healthDemographyGerontologyPublic healthMedicinePopulationGeographyEconomicsPsychiatrySociologyEconomic growth

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the effects of provincial income inequality (disparity between rich and poor), independent of provincial income and family socioeconomic status, on multiple adolescent health outcomes. METHODS: Participants (aged 12-17 years; N = 11,899) were from the Canadian National Longitudinal Survey of Children and Youth. Parental education, household income, province income inequality, and province mean income were measured. Health outcomes were measured across a number of domains, including self-rated health, mental health, health behaviors, substance use behaviors, and physical health. RESULTS: Income inequality was associated with injuries, general physical symptoms, and limiting conditions, but not associated with most adolescent health outcomes and behaviors. Income inequality had a moderating effect on family socioeconomic status for limiting conditions, hyperactivity/inattention, and conduct problems, but not for other outcomes. CONCLUSIONS: Province-level income inequality was associated with some physical and mental health outcomes in adolescents, which has research and policy implications for this age-group.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.384
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
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 head0.080
GPT teacher head0.426
Teacher spread0.345 · 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

Citations24
Published2014
Admission routes2
Has abstractyes

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