Community pharmacist-administered injectable naltrexone for individuals who were formerly incarcerated: a review of Wisconsin legislation and regulations
Bibliographic record
Abstract
Opioid use disorder (OUD) is highly prevalent among jail and prison populations in the United States, including in Wisconsin. Medications for opioid use disorder (MOUD), including long-acting injectable naltrexone, are crucial in the treatment of OUD. These medications are especially important for individuals transitioning out of jail or prison and back into the community. Unfortunately, few individuals who were formerly incarcerated continue MOUD after reentry due to a variety of barriers. Wisconsin community pharmacists are highly accessible and uniquely positioned to provide care for this population, specifically by administering injectable naltrexone. However, community pharmacist-administered injectable naltrexone for individuals who were formerly incarcerated has not been previously explored. As a first step, this legislative and regulatory review aimed to identify Wisconsin statutes and administrative codes that may impact these services for this population. Two legal databases were searched to identify relevant Wisconsin statute and administrative code subsections. Overall, 24 statute subsections (from 7 chapters) and 31 administrative code subsections (from 12 chapters) were identified that (1) highlighted a need or potential role of community pharmacist-administered injectable naltrexone for individuals who were formerly incarcerated or (2) served as a potential barrier or facilitator to the availability, access, or use of these services. Future work should focus on helping community pharmacists leverage available resources and overcome existing legal barriers to providing or supporting MOUD services. Importantly, work should be done to ensure that individuals who were formerly incarcerated can be linked to these services upon reentry.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".