MétaCan
Menu
Back to cohort
Record W2594654097 · doi:10.1007/s12160-017-9891-3

Resilience Resources Moderate the Association of Adverse Childhood Experiences with Adulthood Inflammation

2017· article· en· W2594654097 on OpenAlexafffund
Jean‐Philippe Gouin, Warren Caldwell, Robbie Woods, William B. Malarkey

Bibliographic record

VenueAnnals of Behavioral Medicine · 2017
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsConcordia University
FundersNational Center for Complementary and Integrative HealthNational Center for Advancing Translational SciencesNational Institutes of HealthCanada Research Chairs
KeywordsAdverse Childhood ExperiencesPsychological resilienceMedicineHealth psychologyInflammationYoung adultSystemic inflammationAdverse effectPsychologyClinical psychologyInternal medicinePublic healthPsychiatryMental healthPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Exposure to adverse childhood experiences (ACE) has been associated with elevated circulating inflammatory markers in adulthood. Despite the robust effect of ACE on later health outcomes, not all individuals exposed to ACE suffer from poor health. PURPOSE: The goal of this study was to evaluate whether current resilience resources may attenuate the impact of ACE on inflammatory markers among individuals with elevated C-reactive protein (CRP) levels. METHODS: Participants (N = 174) completed one-time self-report questionnaires assessing ACE exposure within the first 18 years of life and current resilience resources, and provided blood samples for interleukin-6 (IL-6) and CRP. RESULTS: Individuals who were exposed to multiple ACE had greater IL-6 than participants with lesser ACE exposure. However, current resilience resources significantly moderated this effect. Among individuals who reported multiple ACE, higher resilience resources were associated with lower IL-6 levels. CONCLUSION: These data suggest that resilience resources might attenuate the association between ACE and later health outcomes.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.626
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.051
GPT teacher head0.365
Teacher spread0.315 · 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

Citations41
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueAnnals of Behavioral MedicineSame topicChild Abuse and TraumaFrench-language works237,207