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Record W4403647284 · doi:10.1007/s10903-024-01638-x

Disparities in Child Development by Maternal Birthplace and Child Sex among Kindergarten Children in Manitoba, Canada: A Population-Based Data Linkage Study

2024· article· en· W4403647284 on OpenAlexafffundabout
Marcelo L. Urquía, Andrée-Anne Fafard St-Germain, Maria Paula Godoy, Marni Brownell, Magdalena Janus

Bibliographic record

VenueJournal of Immigrant and Minority Health · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsUniversity of ManitobaMcMaster UniversityUniversity of TorontoPublic Health OntarioManitoba Health
FundersInstitute of Gender and HealthCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsLinkage (software)Public healthPopulationEnvironmental healthDemographyMedicineGeneticsSociologyBiology

Abstract

fetched live from OpenAlex

Little is known about differences in child developmental vulnerability before school entry according to maternal birthplace and sex. Official immigration records were linked with the Early Development Instrument assessments among children in kindergarten in the province of Manitoba, Canada (2005-2017). Logistic regression was used to estimate odds ratios of vulnerability in five developmental domains associated with maternal birthplace and child sex. Children of immigrant mothers from most birthplaces had higher adjusted odds of developmental vulnerability than non-immigrants in domains related to language and communication skills, except those of the rest of North America & Oceania. Children of Sub-Saharan African mothers were more vulnerable in four domains. Boys were consistently more vulnerable than girls across domains and maternal birthplaces. Children of immigrant mothers exhibited higher developmental vulnerability than non-immigrants in domains related to language and communication skills, potentially reflecting exposure to English and French as second languages.

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)0.002
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 score0.032
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.022
GPT teacher head0.274
Teacher spread0.252 · 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

Citations0
Published2024
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Immigrant and Minority HealthSame topicDemographic Trends and Gender PreferencesFrench-language works237,207