Examining the Prospects for Developing a National Child Maltreatment Surveillance System in Ireland
Bibliographic record
Abstract
Abstract The Irish Government pledged to reducing the prevalence of child maltreatment under the WHO Regional Committee for Europe plan on reducing child maltreatment. As a first step towards a rights-based and public health approach to maltreatment prevention, the WHO plan recommends making child maltreatment more visible across the region, with better surveillance through the use of national surveys that use standardized, validated instruments. We review the policy context, present current Irish data holdings, and outline some of the complexities reported in the literature concerning various surveillance methods in the context of the proposal to establish and maintain a surveillance system for child maltreatment in Ireland. Conclusions highlight the need for Ireland to adopting an approach to surveillance as soon as it is feasible. The paper outlines how such a programme is necessary to address the current absence of evidence on which prevention policies can be developed and to compliment the current child protection system. Drawing on a review of current methods in use internationally, we outline options for an Irish child maltreatment surveillance programme.
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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 | 0.145 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".