Examining the Spatial Concentration of Mental Health Calls for Police Service in a Small City
Bibliographic record
Abstract
Abstract In recent years, police services have begun deploying more robust responses to calls for service involving persons with perceived mental illness (PwPMI), but at times do so in a limited capacity because of various challenges. Drawing from established evidence-based policing practices, a more efficient use of these responses may be to proactively deploy them instead, focusing their efforts on hot spots of PwPMI calls. Unfortunately, little is known about PwPMI call concentrations. Therefore, this study seeks to contribute to the literature by not only examining the concentration of these calls within a small city, but also by introducing new methods and a new measure of concentration to the literature. Drawing on 6 years of calls for service data, the results reveal that a high proportion of PwPMI calls are concentrated in few spatial units—more so than in larger jurisdictions. Further analyses also reveal dispersion of these concentrations.
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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 | 0.002 | 0.002 |
| 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".