Youth self-report of child maltreatment in representative surveys: a systematic review
Bibliographic record
Abstract
INTRODUCTION: This systematic review identified population-representative youth surveys containing questions on self-reported child maltreatment. Data quality and ethical issues pertinent to maltreatment data collection were also examined. METHODS: A search was conducted of relevant online databases for articles published from January 2000 through March 2016 reporting on population-representative data measuring child maltreatment. Inclusion criteria were established a priori; two reviewers independently assessed articles to ensure that the criteria were met and to verify the accuracy of extracted information. RESULTS: A total of 73 articles reporting on 71 surveys met the inclusion criteria. A variety of strategies to ensure accurate information and to mitigate survey participants' distress were reported. CONCLUSION: The extent to which efforts have been undertaken to measure the prevalence of child maltreatment reflects its perceived importance across the world. Data on child maltreatment can be effectively collected from youth, although our knowledge of best practices related to ethics and data quality is incomplete.
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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 | 0.021 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| 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".