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Record W4410098194 · doi:10.26719/2025.31.4.210

Cases of trauma due to war and violence among children in Gaza

2025· article· en· W4410098194 on OpenAlexaff
Enas Abdelraof A Abu Muaileq, Hani Chaabo, Fatima Abdul Rashid, Mairead Kelly, Neil M. Fournier

Bibliographic record

VenueEastern Mediterranean Health Journal · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsTrent University
Fundersnot available
KeywordsMental healthPsychological interventionGriefMedicineArmed conflictSuicide preventionPsychiatryOccupational safety and healthPoison controlPsychologyMedical emergencyPolitical science

Abstract

fetched live from OpenAlex

Background: The conflict in Gaza has exposed children to continuous cycles of violence and trauma, profoundly affecting their mental health. Aim: To illustrate the psychological burden on Gaza's children and highlight their complex mental health needs amid the conflict. Methods: We report cases of 4 children receiving support from Children Not Numbers, who have lived through the violence in Gaza, illustrating the devastating impact of the conflict on their mental health. Results: The children exhibited symptoms of trauma, including emotional dysregulation, social withdrawal, grief, and worsening of pre-existing conditions. Common factors were displacement, loss of family members, physical injuries, and lack of mental health care resources. Conclusion: The cases highlight the severe psychological impact of the conflict on children and the urgent need for interventions to address the crisis and provide mental health services to prevent long-term consequences for the wellbeing of children in Gaza.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0060.003
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0030.003
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 head0.069
GPT teacher head0.418
Teacher spread0.349 · 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 designCase report
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

Citations5
Published2025
Admission routes1
Has abstractyes

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