Law enforcement and mental health clinician partnerships in global mental health: outcomes for the Crisis Intervention Team (CIT) model adaptation in Liberia, West Africa
Bibliographic record
Abstract
BACKGROUND: The Crisis Intervention Team (CIT) model is a law enforcement strategy that aims to build alliances between the law enforcement and mental health communities. Despite its success in the United States, CIT has not been used in low- and middle-income countries. This study assesses the immediate and 9-month outcomes of CIT training on trainee knowledge and attitudes. METHODS: Twenty-two CIT trainees (14 law enforcement officers and eight mental health clinicians) were evaluated using pre-developed measures assessing knowledge and attitudes related to mental illness. Evaluations were conducted prior to, immediately after, and 9 months post training. RESULTS: The CIT training produced improvements both immediately and 9 months post training in knowledge and attitudes, suggesting that CIT can benefit law enforcement officers even in extremely low-resource settings with limited specialized mental health service infrastructure. CONCLUSION: These findings support further exploration of the benefits of CIT in highly under-resourced settings.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".