Police officers' views of specialized intimate partner violence training
Bibliographic record
Abstract
Purpose A variety of strategies have been developed with the goal of improving justice responses to intimate partner violence. Among these are increasing demands for specialized training of justice professionals. This paper sets out to describe the development of a specialized training program for police officers, drawing attention to the role played by a strong partnership and collaborative approach. Design/methodology/approach Focus groups were held in the winter of 2008 with 30 police officers employed by a municipal police agency who had participated in specialized training on intimate partner violence. Findings As part of a follow‐up to the delivery of training, focus groups examined the impact of specialized training on the preparedness of officers and drew attention to existing challenges in policing intimate partner violence from their perspective. Drawing on earlier studies, the paper makes an important contribution to the law enforcement training literature, illustrating that key to successful development and delivery of specialized police training are extensive partnership and collaborative approaches throughout the initiative. Research limitations/implications The paper is limited by the missing voices of victims and whether or not they perceive a difference in officer response to intimate partner violence as a result of specialized training. In addition, the sample size is relatively small and thus the findings may not be generalizable to a larger sampling. Further, this paper is based on follow‐up only one year after training was implemented at the pilot stage. The work does not tell whether specialized training makes a difference over time or whether training is more effective when continuous. Therefore, the analysis must also be extended to police files, highlighting police responses to such calls. Practical implications Policing services have had to make intimate partner violence a priority. Given the number of calls to police for intervention and the risk of danger, more attention has been placed on specialized training, collaboration across academic and community sectors, as well as changes to legislation. This training is meant to complement existing police training initiatives and enhance awareness about some of the complex issues involved in police intervention. Originality/value With its focus on the voices and experiences of police officers responding to intimate partner violence calls, the paper addresses a gap in the literature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".