Police and partners: new ways of working together in Montréal
Bibliographic record
Abstract
Purpose The purpose of this paper is to present an inter-agency practice integrated within a police intervention model which was developed for police officers and their partners in Montréal. Design/methodology/approach The Integrated Police Response for Abused Seniors (IPRAS) action research project (2013-2016) developed, tested, and implemented a police intervention model to counter elder abuse. Two linked phases of data collection were carried out: a diagnostic of police practices and needs (year 1) and an evaluation of the implementation of the intervention model and the resulting effects (years 2 and 3). Findings The facilitating elements to support police involvement in inter-agency practices include implementing a coordination structure regarding abuse cases as well as designating clear guidelines of the roles of both the police and their partners. The critical challenges involve staff turnover, time management and the exchange of information. It was recognised by all involved that it is crucial to collaborate while prioritising resource investment and governmental support, with regards to policy and financing, as well as adequate training. Practical implications The IPRAS model is transferable because its components can be adapted and implemented according to different police services. A guideline for implementing the model is available. Originality/value In the scientific literature, inter-agency collaboration is highly recommended but only a few models have been evaluated. This paper presents an inter-agency approach embedded in an evaluated police intervention model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".