Law Enforcement and Critical Incident Training
Bibliographic record
Abstract
This research study is a convergent mixed methods design study focusing on Critical Incident Training and Law Enforcement involvement with People with Mental Illness (PwMI). The Southern New Hampshire police department in this study has approximately 201 officers and the city has a population of around 114,00 citizens. The exact number of people with mental illness (PwMI) is unknown due to unreported illnesses. Approximately 1,000 people in the United States were fatally shot by police officers during 2018, and people with mental illness (PwMI) were involved in 25 percent of these fatalities (Rogers MS, McNeil DE, Binder RL, 2019). Critical Incident Training (CIT) teaches law enforcement how to understand, interact, and aid a PwMI. CIT is a 40-hour training class taught by mental health professionals to aid officers with the tools they need. A CIT team is comprised of a mental health worker and a police officer and together they go into the community when needed and bring mental health aid to PwMI. The CIT certified officers in this study completed a Qualtrics survey of 25 questions and eight of the CIT officers were interviewed to find out their thoughts to 14 questions. This was done to find out if CIT is good mental health training for this Southern NH police department. The CIT officers involved in this study were in favor of their coworkers being trained in CIT as it was deemed a useful tool to add to their skillset.
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 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 | 0.008 | 0.031 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".