Police Officers Do Not Need More Training; But Different Training. Policing Domestic Violence and Abuse Involving Children: A Rapid Review
Bibliographic record
Abstract
Abstract Although the police have been identified as a key service provider when responding to domestic violence and abuse (DVA), very few studies have investigated their response in relation to children. This review aims to examine children’s experiences of police response in the context of DVA and to explore how the police understand and respond to children living with DVA. A rapid review of the empirical literature on the police response to DVA involving children was undertaken. PsycINFO, Web of Science and ProQuest were searched. Studies with a qualitative element, concerning children under 18 with experience of police involvement, or police experiences of children, in the context of DVA were included. The final sample comprised of six studies. Using reflexive thematic analysis, four key themes emerged in relation to children: children’s experiences of DVA; fear, uncertainty, and mistrust of police; confronting “childism”: a matter of children’s rights; and going beyond empathy: equality and justice. Regarding the police, three key themes emerged: variability in police response; limited view of police role; lack of professional competence. The findings underscore the need for awareness raising and an urgent review of the training officers receive regarding the impact of DVA on children. They also highlight the pivotal role of police when responding to DVA where children are present, as well as to advance the frontiers of research by including not only adults and professionals but also the most vulnerable DVA victim: the child.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".