On the nature and scope of reported child maltreatment in high-income countries: opportunities for improving the evidence base
Bibliographic record
Abstract
Although high-income countries share and value the goal of protecting children from harm, national data on child maltreatment and the involvement of social services, the judiciary and health services remain relatively scarce. To explore potential reasons for this, a number of high-income countries across the world (Belgium, Canada, Germany, the Netherlands, New Zealand, South Korea, Switzerland and the United States) were compared. Amongst other aspects, the impact of service orientation (child protection-vs-family-services-orientated), the complexity of systems, and the role of social work as a lead profession in child welfare are discussed. Special consideration is given to indigenous and minority populations. The call for high-income countries to collect national data on child maltreatment is to promote research to better understand the risks to children. Its remit ranges well beyond these issues and reflects a major gap in a critical resource to increase prevention and intervention in these complex social situations. Fortunately, initiatives to close this gap are increasing.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".