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School Readiness Among Children Born Preterm in Manitoba, Canada

2022· article· en· W4290659509 on OpenAlexaffabout
Deepak Louis, Sapna Oberoi, M. Florencia Ricci, Christy Pylypjuk, Ruben Alvaro, Mary Seshia, Cecilia de Cabo, Diane Moddemann, Lisa M. Lix, Allan Garland, Chelsea Ruth

Bibliographic record

VenueJAMA Pediatrics · 2022
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsManitoba HealthUniversity of Manitoba
Fundersnot available
KeywordsMedicineCohortPediatricsPopulationLogistic regressionGestational ageDemographyCohort studyPregnancyEnvironmental health

Abstract

fetched live from OpenAlex

Importance: Children born preterm may experience learning challenges at school. However, there is a paucity of data on the school readiness of these children as they prepare to begin grade 1. Objective: To examine the association between prematurity and school readiness in a population-based cohort of children. Design, Setting, and Participants: This cohort study was conducted in the province of Manitoba, Canada, and involved 2 cohorts of children in kindergarten at the time of data collection. The population-based cohort included children born between January 1, 2000, and December 31, 2011, whose school readiness was assessed in kindergarten using the Early Development Instrument (EDI) data. The sibling cohort comprised children born preterm and their closest-in-age siblings born full term. Data were analyzed between March 12 and September 28, 2021. Exposures: Preterm birth, defined as gestational age (GA) less than 37 weeks. Main Outcomes and Measures: The primary outcome was vulnerability in the EDI, defined as a score below the tenth percentile of the Canadian population norms for any 1 or more of the 5 EDI domains (physical health and well-being, social competence, emotional maturity, language and cognitive development, and communication skills and general knowledge). Logistic regression models were used to identify the factors associated with vulnerability in the EDI. P values were adjusted for multiplicity using the Simes false discovery method. Results: Of 86 829 eligible children, 63 277 were included, of whom 4352 were preterm (mean [SD] GA, 34 [2] weeks; 2315 boys [53%]) and 58 925 were full term (mean [SD] GA, 39 (1) weeks; 29 885 boys [51%]). Overall, 35% of children (1536 of 4352) born preterm were vulnerable in the EDI compared with 28% of children (16 449 of 58 925) born full term (adjusted odds ratio [AOR], 1.32; 95% CI, 1.23-1.41; P < .001]). Compared with children born full term, those born preterm had a higher percentage of vulnerability in each of the 5 EDI domains. In the population-based cohort, prematurity (34-36 weeks' GA: AOR, 1.23 [95% CI, 1.14-1.33]; <34 weeks' GA: AOR, 1.72 [95% CI, 1.48-1.99]), male sex (AOR, 2.24; 95% CI, 2.16-2.33), small for gestational age (AOR, 1.31; 95% CI, 1.23-1.40), and various maternal medical and sociodemographic factors were associated with EDI vulnerability. In the sibling cohort, EDI outcomes were similar for both children born preterm and their siblings born full term except for the communication skills and general knowledge domain (AOR, 1.39; 95% CI, 1.07-1.80) and Multiple Challenge Index (AOR, 1.43; 95% CI, 1.06-1.92), whereas male sex (AOR, 2.19; 95% CI, 1.62-2.96) and maternal age at delivery (AOR, 1.53; 95% CI, 1.38-1.70) were associated with EDI vulnerability. Conclusions and Relevance: Results of this study suggest that, in a population-based cohort, children born preterm had a lower school-readiness rate than children born full term, but this difference was not observed in the sibling cohort. Child and maternal factors were associated with lack of school readiness among this population-based cohort.

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)0.000
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 score0.036
Threshold uncertainty score0.821

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.201
Teacher spread0.194 · 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

Citations17
Published2022
Admission routes2
Has abstractyes

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