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Record W1986597170 · doi:10.1080/0145935x.2013.766067

Laying Down the Family Burden: A Cross-Cultural Analysis of Resilience in the Midst of Family Violence

2013· article· en· W1986597170 on OpenAlexaff
Wassilis Kassis, Sibylle Artz, Stephanie Moldenhauer

Bibliographic record

VenueChild & Youth Services · 2013
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAggressionDomestic violenceSocioeconomic statusPsychologyPsychological resilienceFamily resiliencePoison controlSuicide preventionLogistic regressionInjury preventionHuman factors and ergonomicsClinical psychologyDevelopmental psychologyEnvironmental healthSocial psychologyMedicine

Abstract

fetched live from OpenAlex

Questionnaire data from a cross-sectional study of a randomly selected sample of 5,149 middle-school students from four EU countries (Austria, Germany, Slovenia, and Spain) were used to explore the effects of family violence burden level, structural and procedural risk and protective factors, and personal characteristics on adolescents who are resilient to depression and aggression despite being exposed to domestic violence. Using logistic regression to identify resilience characteristics, our results indicate that structural risks like one's sex, migration experience, and socioeconomic status were not predictive of either family violence burden levels or resilience. Rather, nonresilience to family violence is derived from a combination of negative experiences with high levels of family violence in conjunction with inconsistent parenting, verbally aggressive teachers, alcohol and drug misuse and experiences of indirect aggression with peers. Overall, negative factors outweigh positive factors and play a greater role in determining the resilience level that a young person achieves.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.044
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.359
Teacher spread0.337 · 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.

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

Citations21
Published2013
Admission routes1
Has abstractyes

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