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Record W4285391898 · doi:10.1111/cdep.12462

The Bucharest Early Intervention Project: Adolescent mental health and adaptation following early deprivation

2022· article· en· W4285391898 on OpenAlexaff
Mark Wade, Jill Petraglia Parsons, Kathryn L. Humphreys, Katie A. McLaughlin, Margaret A. Sheridan, Charles H. Zeanah, Charles A. Nelson, Nathan A. Fox

Bibliographic record

VenueChild Development Perspectives · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Mental HealthBinder Family Foundation
KeywordsPsychologyMental healthPsychopathologyIntervention (counseling)Developmental psychologySocial deprivationDevelopmental psychopathologyChild developmentClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Over the last 20 years, we have learned much about the extent to which early-life deprivation affects the mental health of children and adolescents. This body of evidence comes predominantly from studies of children raised in institutional care. The Bucharest Early Intervention Project (BEIP) is the only randomized controlled trial designed to evaluate whether the transition to family-based foster care early in development can ameliorate the long-term impact of institutional deprivation on psychopathology during vulnerable developmental windows such as adolescence. In this review, we detail the extent to which early deprivation affects mental health during this period, the capacity of family-based care to facilitate recovery from early deprivation, and the mechanisms underpinning these effects spanning social-emotional, cognitive, stress, and neurobiological domains. We end by discussing the implications and directions for the BEIP and other studies of youth raised in institutions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.294
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), 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

Citations30
Published2022
Admission routes1
Has abstractyes

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