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Record W2770676028 · doi:10.1017/s0954579417001651

How do childhood intelligence and early psychosocial adversity influence income attainment among adult extremely low birth weight survivors? A test of the cognitive reserve hypothesis

2017· article· en· W2770676028 on OpenAlexafffund
Kathleen G. Dobson, Mark A. Ferro, Michael H. Boyle, Louis A. Schmidt, Saroj Saigal, Ryan J. Van Lieshout

Bibliographic record

VenueDevelopment and Psychopathology · 2017
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsUniversity of WaterlooMcMaster UniversityUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsPsychologySocioeconomic statusPsychosocialNeuroticismLow birth weightDevelopmental psychologyCognitionIntelligence quotientClinical psychologyDemographyPersonalityPsychiatryPregnancyPopulation

Abstract

fetched live from OpenAlex

Perinatal and later postnatal adversities have been shown to adversely affect socioeconomic trajectories, while enhanced early cognitive abilities improve them. However, little is known about the combined influence of these exposures on social mobility. In this study, we examined if childhood IQ moderated the association between four different types of postnatal adversity (childhood socioeconomic disadvantage, childhood sexual abuse, lifetime psychiatric disorder, and trait neuroticism) and annual earnings at 30-35 years of age in a sample of 88 extremely low birth weight survivors. Our results suggested that higher childhood IQ was associated with greater personal income at age 30-35. Extremely low birth weight survivors who did not face psychological adversities and who had higher childhood IQ reported higher income in adulthood. However, those who faced psychological adversity and had higher childhood IQ generally reported lower income in adulthood. Our findings suggest that cognitive reserve may not protect preterm survivors against the complex web of risk factors affecting their later socioeconomic attainment.

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)0.001
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 score0.033
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.021
GPT teacher head0.263
Teacher spread0.241 · 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

Citations3
Published2017
Admission routes2
Has abstractyes

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