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Record W4390885455 · doi:10.3390/healthcare12020203

How Does Trauma Make You Sick? The Role of Attachment in Explaining Somatic Symptoms of Survivors of Childhood Trauma

2024· review· en· W4390885455 on OpenAlexaff
Paul S. Greenman, Alessia Renzi, Silvia Monaco, Francesca Luciani, Michela Di Trani

Bibliographic record

VenueHealthcare · 2024
Typereview
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsInstitut du Savoir MontfortUniversité du Québec en Outaouais
Fundersnot available
KeywordsSomatizationAdverse Childhood ExperiencesMedicineDiseasePhysical abuseNeglectChild abusePsychiatrySexual abuseAnxietyClinical psychologyPsychologyMental healthPoison controlInjury preventionMedical emergency

Abstract

fetched live from OpenAlex

Exposure to traumatic events during childhood is common, and the consequences for physical and mental health can be severe. Adverse childhood experiences (ACEs) such as physical abuse, sexual abuse, emotional abuse, and neglect appear to contribute to the onset and severity of a variety of somatic inflictions, including obesity, diabetes, cancer, and heart disease. The aim of this scoping review was to try to gain insight into how this might occur. Given the evidence of indirect (i.e., through unhealthy behaviours such as excessive drinking or poor eating habits) and direct (i.e., through its impact on the endocrine, immune, and cardiovascular systems as well as on the brain) effects of attachment on health, we examined the possibility that insecure attachment might contribute to the development of somatic symptoms in adult survivors of childhood trauma. Eleven studies met our inclusion criteria. Findings from this review suggest that insecure and disorganized attachment orientations are related to DNA damage, metabolic syndrome and obesity, physical pain, functional neurological disorder, and somatization in adults exposed to childhood trauma. We discuss the implications of this for the conceptualization and treatment of trauma and stress disorders.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.940
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.041
GPT teacher head0.356
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.

Study designOther design
Domainnot available
GenreReview

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

Citations14
Published2024
Admission routes1
Has abstractyes

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