A Systematic Review of Childhood Maltreatment Assessments in Population-Representative Surveys Since 1990
Bibliographic record
Abstract
BACKGROUND: Population-representative surveys that assess childhood maltreatment and health are a valuable resource to explore the implications of child maltreatment for population health. Systematic identification and evaluation of such surveys is needed to facilitate optimal use of their data and to inform future research. OBJECTIVES: To inform researchers of the existence and nature of population-representative surveys relevant to understanding links between childhood maltreatment and health; to evaluate the assessment of childhood maltreatment in this body of work. METHODS: We included surveys that: 1) were representative of the non-institutionalized population of any size nation or of any geopolitical region ≥ 10 million people; 2) included a broad age range (≥ 40 years); 3) measured health; 4) assessed childhood maltreatment retrospectively; and 5) were conducted since 1990. We used Internet and database searching (including CINAHL, Embase, ERIC, Global Health, MEDLINE, PsycINFO, Scopus, Social Policy and Practice: January 1990 to March 2014), expert consultation, and other means to identify surveys and associated documentation. Translations of non-English survey content were verified by fluent readers of survey languages. We developed checklists to abstract and evaluate childhood maltreatment content. RESULTS: Fifty-four surveys from 39 countries met inclusion criteria. Sample sizes ranged from 1,287-51,945 and response rates from 15%-96%. Thirteen surveys assessed neglect, 15 emotional abuse; 18 exposure to family violence; 26 physical abuse; 48 sexual abuse. Fourteen surveys assessed more than three types; six of these were conducted since 2010. In nine surveys childhood maltreatment assessments were detailed (+10 items for at least one type of maltreatment). Seven surveys' assessments had known reliability and/or validity. CONCLUSIONS AND IMPLICATIONS: Data from 54 surveys can be used to explore the population health relevance of child maltreatment. Assessment of childhood maltreatment is not comprehensive but there is evidence of recent improvement.
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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 | 0.033 | 0.151 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.029 | 0.037 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".