Transforming first response through non-police, community safety response programmes: a peer-reviewed and grey literature scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Police are frequently dispatched to a wide range of 911 calls, including mental and behavioural health crises, despite lacking the training, resources and time to respond effectively. In particular, people with serious mental illness are at elevated risk of experiencing excessive use of force, arrest and continued criminal legal involvement following police contact. Following the murder of George Floyd and other highly publicised police killings, Community Safety Response (CSR) programmes, staffed by unarmed peers, mental health professionals and other trained responders, have proliferated to provide non-police responses to mental and behavioural health and other quality-of-life concerns. CSR programmes have expanded rapidly, yet the evidence base remains fragmented and largely outside the peer-reviewed literature. METHODS AND ANALYSIS: This scoping review will synthesise peer-reviewed and grey literature from 2020 to present on CSR programmes operating in North America. Guided by Joanna Briggs Institute methodology and reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) standards, we will search multiple databases (Medline, PsycINFO, Embase, SocIndex, Web of Science, Policy Commons) and employ complementary grey literature search strategies, including targeted website searches, reference tracking and review of internal and external reports and evaluations. Inclusion criteria require that programmes provide non-police first response to calls traditionally served by law enforcement and include information on programme operations or outcomes. Two reviewers will independently screen and extract data on process metrics including operational characteristics, dispatch, funding, services provided and outcomes such as populations served, diversion from police, service linkage and use of force. ETHICS AND DISSEMINATION: No ethical review for this study is required as it will not include human subjects or any identifiable information. Findings will provide the first national synthesis of CSR programme models, operations and outcomes. Results will inform policy-makers, practitioners, researchers and community members. Findings will be disseminated through peer-reviewed publications and public-facing products to support implementation, scale-up and sustainability of CSR programmes.
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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 | 0.171 | 0.148 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.020 | 0.015 |
| Bibliometrics | 0.029 | 0.022 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.014 | 0.008 |
| Insufficient payload (model declined to judge) | 0.076 | 0.022 |
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".