Editorial: Identifying and addressing the impact of exposure to maltreatment and experience in children and child serving systems of care
Bibliographic record
Abstract
Editorial on the Research TopicIdentifying and addressing the impact of exposure to maltreatment and experience in children and child serving systems of care Child maltreatment is prevalent and contributes to a wide range of emotional and behavioral issues across one's lifespan.The extant literature on child maltreatment includes its epidemiology, neurobiology, clinical impacts, and related treatments.Over the last several years, increasing attention has been placed on the experiences and impacts of systems of care for children who have been exposed to maltreatment.It was in this context that Frontiers solicited the manuscripts for this Research Topic.In reviewing the work of the 12 teams who submitted manuscripts for this Research Topic, we noted several themes, each of which represents a lesson from the authors and a call for ongoing investigation into understanding how to identify and address risk factors for maltreatment, recognize those affected, and organize systems of care more effectively to provide support.Although specific works are highlighted in each lesson, a careful reading of the manuscripts in this Research Topic reflects each of the themes outlined below. Lesson 1Research must reflect the risks and patterns of maltreatment worldwide.Naved et al. link social determinants, including a more patriarchal culture, to the risk of exposure to violence among boys and girls.Wakuta et al. focus on traumatic interactions in school settings and Zhang et al. explore the impact of parental protection/overcontrol as a risk on the experiences of university students in China.Although not directly examining maltreatment, Au-Yeung et al. describe important work to support the well-being of Indigenous youth.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.022 | 0.021 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".