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The lasting imprint of childhood disadvantage: cumulative histories of exposure to childhood adversity and trajectories of psychological distress in adulthood

2021· article· en· W3181449343 on OpenAlexaff
Loanna Heidinger, Andrea E. Willson

Bibliographic record

VenueLongitudinal and Life Course Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychologyDisadvantageLife course approachMental healthDistressPsychological distressDevelopmental psychologyEarly childhoodLatent class modelYoung adultEarly adulthoodPanel Study of Income DynamicsHistory of childhoodClinical psychologyPsychiatryInjury preventionPoison controlChild abuseMedicine

Abstract

fetched live from OpenAlex

This study contributes to the literature on the long-term effects of childhood disadvantage on mental health by estimating the association between patterns of cumulative childhood adversity on trajectories of psychological distress in adulthood. There is little research that investigates how compositional variations in the accumulation of childhood adversity may initiate distinct processes of disadvantage and differentially shape trajectories of psychological distress across the adult life course. Using the Panel Study of Income Dynamics' Childhood Retrospective Circumstance Study and latent class analysis, we first identify distinct classes representing varied histories of exposure to childhood adversities using 25 indicators of adversity across multiple childhood domains. Next, the latent classes are included as predictors of trajectories of psychological distress in adulthood. The results demonstrate that patterns of experiences of childhood adversity are associated with higher levels of adult psychological distress that persists, and in some cases worsens, in adulthood, contributing to disparities in mental health across the life course.

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)0.002
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 score0.076
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.035
GPT teacher head0.368
Teacher spread0.333 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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