Asking youth and adults about child maltreatment: a review of government surveys
Bibliographic record
Abstract
OBJECTIVES: In this review we: (1) identify and describe nationally representative surveys with child maltreatment (CM) questions conducted by governments in low-income, middle-income and high-income countries and (2) describe procedures implemented to address respondents' safety and minimise potential distress. DESIGN: We conducted a systematic search across eight databases from 1 January 2000 to 5 July 2021 to identify original studies with information about relevant surveys. Additional information about surveys was obtained through survey methods studies, survey reports, survey websites or by identifying full questionnaires (when available). RESULTS: Forty-six studies representing 139 surveys (98 youth and 41 adult) conducted by governments from 105 countries were identified. Surveys implemented a variety of procedures to maximise the safety and/or reduce distress for respondents including providing the option to withdraw from the survey and/or securing confidentiality and privacy for the respondent. In many surveys, further steps were taken such as providing information for support services, providing sensitivity training to survey administrators when interviews were conducted, among others. A minority of surveys took additional steps to empirically assess potential distress experienced by respondents. CONCLUSIONS: Assessing risk and protective factors and developing effective interventions and policies are essential to reduce the burden of violence against children. While asking about experiences of CM requires careful consideration, procedures to maximise the safety and minimise potential distress to respondents have been successfully implemented globally, although practices differ across surveys. Further analysis is required to assist governments to implement the best possible safety protocols to protect respondents in future surveys.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.125 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".