Restructuring Crisis Response Programs as Civilian-Led: A Scoping Review
Bibliographic record
Abstract
Civilian-led crisis response teams provide emergency mental healthcare for individuals in acute distress in community settings, often without the involvement of police as first responders. These models represent one of several emerging alternatives to police-led crisis response and have gained attention for their potential to improve safety, trust, and care outcomes. Reports included in this scoping review discussed: the development, need, potential, implementation, and outcomes of civilian-led crisis response models. Covidence software was utilized by two independent reviewers to search 11 databases, with a third reviewer resolving conflicts. A dataset of 46 reports were then analyzed with thematic content analysis by a multidisciplinary team using critical theories to offer an exploration of how civilian-led crisis teams have begun to address the harms of policing responses to mental health. In exploring the key processes for civilian-led crisis response teams, three themes emerged. The first theme, Decentering Police, explores the growing collective awareness of the harms associated with police-involved crisis intervention and the corresponding need for alternative approaches, alongside efforts to establish a team composition that is intentionally distinct. The second theme, Team Scope of Practice, explores the subthemes of dispatch logistics and defining criteria for response. Team Sustainability is the third theme and explores how social and political will shape the uptake and long-term operation of civilian-led crisis programs.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".