Police responses to intimate partner violence incidents involving children: Exploring variations in actions and concerns in an Australian jurisdiction
Bibliographic record
Abstract
BACKGROUND: Intimate Partner Violence (IPV) has transformed from a private matter into a global concern. Although progress has been made in enhancing police responsiveness to IPV, research on interventions in IPV cases involving children remains limited. OBJECTIVE: This study investigates how police officers' responses vary depending on the nature and severity of IPV incidents and explores disparities in their responses when children are present at IPV incidents. PARTICIPANTS AND SETTING: 175 police officers (126 males, 49 females) in a single Australian jurisdiction. METHODS: A mixed-methods approach utilized an online survey with four hypothetical IPV scenarios to capture anticipated responses. The quantitative analysis assessed officers' recognition of incident seriousness and willingness to take action, while the qualitative thematic analysis explored reasons for response modifications in the presence of children. RESULTS: The quantitative analysis revealed that officers consistently recognized the seriousness of IPV incidents and displayed a willingness to take various actions, such as initiating investigations and detaining perpetrators. Thematic analysis of qualitative data uncovered officers' reasons for modifying or maintaining their responses to IPV incidents with child presence. Concerns for child safety, emotional impact on children, and breaking the cycle of violence were identified as key drivers for officers' modified responses. Additionally, some officers adhered to standard procedures, emphasizing their legal obligations and the adequacy of their existing actions. CONCLUSIONS: This study contributes to an enhanced understanding of the complex decision-making processes among police officers when responding to IPV incidents involving children, highlighting the necessity of balanced policies and comprehensive training to navigate these complexities effectively.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".