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Record W2766448752 · doi:10.1177/0146167217736049

Multiple Dimensions of Childhood Abuse and Neglect Prospectively Predict Poorer Adult Romantic Functioning

2017· article· en· W2766448752 on OpenAlexaff
Madelyn H. Labella, William F. Johnson, Jodi Martin, Sarah K. Ruiz, Jessica Shankman, Michelle M. Englund, W. Andrew Collins, Glenn I. Roisman, Jeffry A. Simpson

Bibliographic record

VenuePersonality and Social Psychology Bulletin · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsYork University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Mental HealthNational Institute on Aging
KeywordsPsychologyNeglectDevelopmental psychologyRomanceLongitudinal studyPhysical abuseChild abuseSexual abuseChildhood abusePoison controlCompetence (human resources)Injury preventionClinical psychologySocial psychologyPsychiatryMedicine

Abstract

fetched live from OpenAlex

The present study used data from the Minnesota Longitudinal Study of Risk and Adaptation (MLSRA) to investigate how multiple dimensions of childhood abuse and neglect predict romantic relationship functioning in adulthood. Several dimensions of abuse and neglect (any experience, type, chronicity, co-occurrence, and perpetrator) were rated prospectively from birth through age 17.5 years. Multimethod assessments of relational competence and violence in romantic relationships were conducted repeatedly from ages 20 to 32 years. As expected, experiencing childhood abuse and neglect was associated with lower romantic competence and more relational violence in adulthood. Follow-up analyses indicated that lower romantic competence was specifically associated with physical abuse, maternal perpetration, chronicity, and co-occurrence, whereas more relational violence was uniquely associated with nonparental perpetration. We discuss these novel prospective findings in the context of theory and research on antecedents of romantic relationship functioning.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
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 head0.026
GPT teacher head0.325
Teacher spread0.299 · 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.

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

Citations44
Published2017
Admission routes1
Has abstractyes

Explore more

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