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Record W4416039671 · doi:10.18357/ijcyfs162-3202522526

FIFTEEN YEARS OF THE “STUDENTS FOR CHILDREN” PROGRAM: TRAUMA-INFORMED VOLUNTEERISM AND INTERDISCIPLINARY COLLABORATION IN CHILD PROTECTION

2025· article· en· W4416039671 on OpenAlexvenueno aff
Beáta Korinek, Judit Zeller, Gabriella Kulcsár, Petra Kondora

Bibliographic record

VenueInternational Journal of Child Youth and Family Studies · 2025
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsChild protectionWelfareBridging (networking)Work (physics)Social workSocial WelfareChild care

Abstract

fetched live from OpenAlex

The Students for Children program has been running for more than 15 years in Hungary, offering university students an opportunity to engage in child protection work through a trauma-informed, interdisciplinary approach. The program serves a dual purpose: supporting children in specialized care while equipping students with practical experience in working with vulnerable populations. Through structured classroom training, supervised volunteer work, and collaborative partnerships with child protection institutions, the program fosters professional preparedness, ethical engagement, and social responsibility. This paper explores the program’s development, key guidelines, and bridging function of linking disciplines, institutions, and international networks to strengthen child welfare efforts. Insights from student testimonials and micro-research findings illustrate the program’s impact, highlighting its role in promoting trauma-informed care, emotional safety, and sustainable professional engagement.

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)0.006
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0080.004
Scholarly communication0.0030.001
Open science0.0020.010
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.021
GPT teacher head0.367
Teacher spread0.347 · 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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