Developing services for a public health approach to child maltreatment
Bibliographic record
Abstract
Calls for a public health approach to child maltreatment – a strategy that aims primarily to reduce risk factors for maltreatment - have been based on four main arguments. (O’Donnellet al. 2008; Reading et al. 2009; Barlow and Calam, 2011) The right of children to be protected from harm in the first place. The frequency of child maltreatment, which, if all occurrences were notified, would overwhelm child protection systems. The inaccuracy of identification systems, which miss the large majority of maltreated children. And fourth, the effectiveness and cost effectiveness of intervening to prevent child maltreatment comparing with intervention once child maltreatment has occurred. We review the evidence to support these arguments and trace the development of UK policy and health services towards a public health approach.
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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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.021 | 0.020 |
| Insufficient payload (model declined to judge) | 0.059 | 0.006 |
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".