Child Maltreatment Reporting Practices by a Person Most Knowledgeable for Children and Youth: A Rapid Scoping Review
Bibliographic record
Abstract
Child maltreatment is a global public health and child rights crisis made worse by the ongoing COVID-19 pandemic. While understanding the breadth of the child maltreatment crisis is foundational to informing prevention and response efforts, determining accurate estimates of child maltreatment remains challenging. Alternative informants (parents, caregivers, a Person Most Knowledgeable-PMK) are often tasked with reporting on children's maltreatment experiences in surveys to mitigate concerns associated with reporting child maltreatment. The overall purpose of this study was to examine child maltreatment reporting practices in surveys by PMKs for children and youth. The research question is: "What is the nature of the evidence of child maltreatment reporting practices in general population surveys by PMKs for children and youth?" A rapid scoping review was conducted to achieve the study's purpose. A search strategy was conducted in nine databases (e.g., MEDLINE, EBSCO, Scopus, Global Health, ProQuest). The findings from this review indicate that most studies involved PMK informants (i.e., maternal caregivers), included representative samples from primarily Western contexts, and utilized validated measures to assess child maltreatment. Half of the studies assessed involved multi-informant reports, including the PMKs and child/youth. Overall, the congruence between PMK-reported and child/youth-reported child maltreatment experiences was low-to-fair/moderate, and children/youth reported more maltreatment than the PMKs.
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 | 0.037 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.048 | 0.035 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".