A DYNAMIC AND GENDER SENSITIVE UNDERSTANDING OF ADOLESCENTS’ PERSONAL AND SCHOOL RESILIENCE CHARACTERISTICS DESPITE FAMILY VIOLENCE: THE PREDICTIVE POWER OF THE FAMILY VIOLENCE BURDEN LEVEL
Bibliographic record
Abstract
In this cross-sectional study on family violence and resilience in a sample of 5,149 middle-school students with a mean age of 14.5 years from four European Union countries (Austria, Germany, Slovenia, and Spain), we worked from the premise that resilience should not be conceptualized as a dichotomous variable. We therefore examined the gender-specific personal and social characteristics of resilience at the three levels “resilient”, “near-resilient”, and “non-resilient”. We also expanded our definition of resilience to include the absence of both externalized and internalized problem behaviours in adolescents who have been exposed to violence in their families. Using multinomial logistic regression we found reliable gender differences in the protective and risk factors between the three resilience levels. We also found that the achieved reliability of our resilience classifications is very high. Our findings suggest that adolescents’ positive adjustment despite family violence is affected only in small part by school characteristics. The co-morbidity of social risks in the family and individual factors explains a much larger part of the variance in the analysis. From a content perspective this means that an individual’s “resilience status” can be influenced in a focused way by moderating the living environment. These results are discussed in terms of their practical implications for policy.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".