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Record W2795927821 · doi:10.1093/pch/pxy043

Adverse childhood experiences: Basics for the paediatrician

2018· article· en· W2795927821 on OpenAlexaff
Gabriella Jacob, Meta van den Heuvel, Nimo Jama, Aideen M. Moore, Lee Ford-Jones, Peter Wong

Bibliographic record

VenuePaediatrics & Child Health · 2018
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsPsychological resilienceAdverse Childhood ExperiencesMedicineDiseaseEarly childhoodAdverse effectPsychological interventionLife course approachDevelopmental psychologyPsychologyPsychiatryMental healthPsychotherapist

Abstract

fetched live from OpenAlex

In 1998, the Centers for Disease Control and Prevention Adverse Childhood Experiences study established the profound effects of early childhood adversity on life course health. The burden of cumulative adversities can affect gene expression, immune system development and condition stress response. A scientific framework provides explanation for numerous childhood and adult health problems and high-risk behaviours that originate in early life. In our review, we discuss adverse childhood experiences, toxic stress, the neurobiological basis and multigenerational and epigenetic transmission of trauma and recognized health implications. Further, we outline building resilience, screening in the clinical setting, primary care interventions, applying trauma-informed care and future directions. We foresee that enhancing knowledge of the far-reaching effects of adverse childhood events will facilitate mitigation of toxic stress, promote child and family resilience and optimize life course health trajectories.

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, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.023
GPT teacher head0.310
Teacher spread0.288 · 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 designNot applicable
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

Citations26
Published2018
Admission routes1
Has abstractyes

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