Child Abuse Incidences Per Child Year Predicted from the Available Average Incidence Combined with Foreign Incidences Per Child Year: Towards a New Policy
Bibliographic record
Abstract
Child abuse is a worldwide recognized serious problem. Reliable child abuse incidences, preferably per child year, are fundamental for a sound detection and prevention program. Unfortunately, in most countries where child abuse data is available, incidences are not determined per child year but as an average over the child age range. In this paper we suggest a possible "next-best" solution for deriving child abuse incidences per child year when only an average value is available in an area or country. As method, we combined the country's measured average incidence with available (foreign) incidences per child year. The country's next-best incidences per child year will be estimated from its average, multiplied by the foreign incidences per child year divided by the foreign average. As results, we calculated the next-best Dutch age-dependent incidences by combining the Dutch average value with US and Ontario age-related incidences. We found comparable results for infants above 1 year and marked differences for children <1 year, likely due to cultural differences between the US and Ontario. In conclusion, next-best age-related child abuse incidences are obtainable in large areas or countries by choosing a smaller but representative region, the latter estimated from Ontario-data as ≥210,000 inhabitants, and establishing as perfectly as possible the optimal infra structure. A future perspective towards a new policy could be to initiate and stimulate this approach in the various European Union and United Nations Convention on the Rights of the Child member states.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".