MétaCan
Menu
← Back to cohort
Record W6958709641 · doi:10.6084/m9.figshare.27754137

Supplementary Material for: Adverse Childhood Experiences are associated with Mental Health Problems later in life: An Umbrella Review of Systematic Review and Meta-analysis

2024· dataset· en· W6958709641 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2024
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicPlant pathogens and resistance mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthContext (archaeology)Sexual abuseMeta-analysisSystematic reviewDomestic violencePhysical abuseChild abusePoison control

Abstract

fetched live from OpenAlex

Abstract Introduction: Evidence suggested a link between early adversity and mental health problems. However, it is unclear how much adverse childhood experiences (ACEs) contribute to mental health problems because researchers have produced inconsistent findings. Therefore, the objective of this umbrella review was to combine the contradictory data regarding the effect of ACEs on the development of mental health problems later in life in the global context. Methods: PubMed, Embase, Scopus, Web of Sciences, Cochrane Database of Systematic Reviews, Scopus, and Google Scholar which reported the effect of ACEs on the development of mental health problems were searched. The quality of the included studies was assessed using the Assessment of Multiple Systematic Reviews (AMSTAR). A weighted inverse variance random-effects model was applied to find the pooled estimates. The subgroup analysis, heterogeneity, publication bias, and sensitivity analysis were also assessed. Results: Forty-three SRM with 14707614 study participants were included. The pooled effect of ACEs on the development of mental health problems later in life in the global context is found to be (AOR=1.66 (1.46, 1.87)). Subgroup analysis based on country revealed (AOR=1.67(1.23, 2.11)) in UK, (AOR=0.61(0.41, 0.81)) in Canada, (AOR=1.55(1.40, 1.69)) in Brazil, (AOR=5.65(4.12, 7.18)) in Ethiopia, (AOR=1.92(1.45, 2.38)) in USA, (AOR=2.30(1.89, 2.72)) in Australia and (AOR=1.66(1.46, 1.87)) in irland. While subgroup analysis based on types of adverse childhood adverse experience: domestic violence ((AOR=4.13(1.96, 6.30)), maltreatment (AOR=1.5(0.79, 2.21)), physical abuse(AOR=1.56(1.43,1.63), sexual abuse (AOR=2.07(1.63, 2.51)), child abuse (AOR=5.66(4.12,7.18)), parental mental health problem (AOR=1.73(1.39,2.08)), bullying (AOR=1.99(1.69, 2.29), neglect(AOR=2.11(1.53,2.69)), and parental divorce (AOR=1.66(1.46,1.87)). Based on the type of mental health problem the pooled effect size is 1.87(1.45, 2.30) for depression and 1.67(1.22, 2.13) for anxiety. Conclusion: This umbrella review revealed that adverse childhood experience is significantly associated (with 66% increased risk) with anxiety and depression later in life in a global context. This association is most noticeable when one is subjected to domestic violence, maltreatment, physical abuse, sexual abuse, child abuse, parental mental health problems, bullying, neglect, and parental divorce. Childhood periods are a critical window of opportunity for reducing the risk of developing mental illness in the future and for implementing intervention measures. Preventing childhood maltreatment and addressing psychiatric risk factors can prevent psychopathology. Longitudinal studies are needed to optimize healthcare responses to ACEs. Increased awareness and public health interventions are needed to prevent childhood adversity and prevent mental problems among these victims. To optimize healthcare responses to unfavorable outcomes of childhood adversities, longitudinal and intervention research findings, more public health initiatives, and awareness are required.

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)0.013
metaresearch head score (Gemma)0.105
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.386
Threshold uncertainty score0.876

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.105
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.009
Bibliometrics0.0170.018
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0030.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.3860.017

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 head0.063
GPT teacher head0.277
Teacher spread0.214 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueFigshare→Same topicPlant pathogens and resistance mechanisms→French-language works237,207→