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Record W2030798449 · doi:10.1002/hec.591

The role of permanent income and family structure in the determination of child health in Canada

2001· article· en· W2030798449 on OpenAlexaffabout
Lori J. Curtis, Martin Dooley, Ellen L. Lipman, David Feeny

Bibliographic record

VenueHealth Economics · 2001
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsUniversity of AlbertaMcMaster UniversityDalhousie University
Fundersnot available
KeywordsProxy (statistics)Demographic economicsHealth Utilities IndexFamily incomeCategorical variableLow incomePermanent income hypothesisEconomicsDemographyHousehold incomeSocioeconomic statusGeographySociologyStatisticsEconomic growthMEDLINEPolitical scienceMathematicsFinance

Abstract

fetched live from OpenAlex

We use data from the Ontario Child Health Study (OCHS) to provide the first Canadian estimates of how the empirical association between child health and both low-income and family status (lone-mother versus two-parent) changes when we re-estimate the model with pooled data. Two waves of data provide a better indication of the family's long-run level of economic resources than does one wave. Our measures of health status include categorical indicators and the health utility score derived from the Health Utilities Index Mark 2 (HUI2) system. Consistent with findings from other countries, we find that most outcomes are more strongly related to low-average income (in 1982 and 1986) than to low-current income in either year. Unlike some previous research, we find the quantitative impact of low-income on child health to be modest to large. Lone-mother status is negatively associated with most outcomes, but the lone-mother coefficients did not change significantly when we switched from low-current income to low-average income. This implies that the lone-mother coefficient in single cross-sections is not just a proxy for low-permanent income.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.048
Threshold uncertainty score0.346

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.341
Teacher spread0.324 · 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 source (direct Gemma or distilled Codex), 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

Citations77
Published2001
Admission routes2
Has abstractyes

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