Identifying barriers and advantages in implementing a drug deflection policy that impacts the role of law enforcement
Bibliographic record
Abstract
This study seeks to gain a deeper understanding of how implementing a drug deflection program through law enforcement creates new challenges as we look to officers to assist community members in accessing treatment. This is an action research study with a generic qualitative inquiry that seeks knowledge about real-life work changes due to new legislative policies. The legislative policy created in 2019 called the Community-Law Enforcement Partnership for Deflection and Substance Use Disorder Treatment Act (CLEPD) encouraged the creation of drug deflection programs within law enforcement agencies in Illinois. The participants identified were law enforcement officers in a suburban county of Illinois implementing a drug deflection program. Included with this population were administrators at the law enforcement agency and treatment professionals. Data was collected through semi-structured interviews, transcribed, and analyzed to identify emergent themes. This study identified two barriers: the need for additional officers to provide transportation and lack of local community resources. The greatest advantages of implementation were positive interactions with the community, administrative support, and an alternative to incarceration.
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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".