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Record W4406854065 · doi:10.24911/ijmdc.51-1735149565

The Influence of Childhood Trauma on Adult Mental Health

2025· article· en· W4406854065 on OpenAlexaboutno aff
Ahmed Abdelsamie Fadl, Refal Alnughaymishi, Naseem Alrawaili, Ghadeer Alqarni, Y Modawi, Nehal Alqurashi, Omniya Ajlan, Othman Alturki, Yasser Al-jaffer, Ahmed Ramadhan, Reem Alhulaisi

Bibliographic record

VenueInternational Journal of Medicine in Developing Countries · 2025
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthPsychologyDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

Childhood trauma can affect an adult's health for a long time, according to research. Using data from the 2005 Canadian Community Health Survey, we investigate the potential roles of adult mental health and socioeconomic position as separate mediators of the association between childhood trauma and chronic illness, with a life course focus on cumulative disadvantage. Scholars are investigating socioeconomic class and mental health as protective factors against the generally negative effects of childhood trauma. Research suggests that both socioeconomic class and mental health, with mental health having a greater impact, can contribute to the relationship between childhood trauma and chronic illness in adulthood. Higher socioeconomic position may also provide more protection against trauma, according to an examination of the associations. The findings also indicate that cumulative disadvantage following trauma may lead to chronic illness, highlighting the importance of public health programs in offering resources such as income assistance and counseling to prevent or reduce psychological harm and chronic illness caused by traumatic experiences.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.012
GPT teacher head0.403
Teacher spread0.391 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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