Minnesota police see sharp drop in MH crisis following new training
Bibliographic record
Abstract
Mental health crisis calls reported by Minnetonka police fell by almost a quarter in 2018, after officers took part in a crisis intervention program developed by two Twin Cities criminal justice professors, the Star Tribune reported Feb. 22. The decline in crisis calls contrasts dramatically with statistics from comparable metro‐area cities, said Jillian Peterson, a professor at Hamline University in St. Paul who co‐developed the training program. Elsewhere, last year's crisis calls — which typically involve depression, suicide threats, psychosis or erratic behavior under the influence of drugs or alcohol — either increased from the previous year or declined only slightly. In Minnetonka, a suburban city in Minnesota, everyone on the police department staff — civilians as well as the 57 sworn officers — was required to take the training in February 2018. Last year's calls totaled 302, which was 23 percent fewer than the previous year. Minnetonka police are planning further improvements in their strategies for handling crisis calls, Minnetonka Police Chief Scott Boerboom said. For example, officials are working on a plan with the Plymouth Police Department to partner with a social worker who would provide follow‐up counseling.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".