Bibliographic record
Abstract
What Is the Issue? Directly observed therapy ensures patient adherence to a prescribed treatment regimen by having a health care worker watch the patient take the drug (s). However, it can result in patients needing to wait to see an available health care provider, and long waits can lead to patients being late for or missing out on work, school, or other therapy. What Did We Do? We conducted a literature search to identify, gather, synthesize, and summarize relevant evidence to inform our understanding of how directly observed therapy is used and what drugs are administered via directly observed therapy in correctional settings in Canada and internationally. What Did We Find? We identified practice manuals and guidelines from Canada, British Columbia, Manitoba, Nova Scotia, Ontario, and Saskatchewan, as well as Australia and the UK, related to using directly observed therapy in correctional settings. Drugs recommended to be provided via directly observed therapy included methadone, other drugs for opioid use disorder, and drugs at high risk of misuse or diversion (e.g., benzodiazepines, opioids). Most documents noted the high risk of misuse and/or diversion, suggesting these are the main justifications for providing drugs via directly observed therapy. Some strategies that may help reduce administration or wait times include an in-possession policy (where patients keep their drugs and self-administer if possible) and long-acting injectable forms of drugs (e.g., buprenorphine, antipsychotics). In-possession is typically not recommended for drugs at high risk of misuse and diversion. Still, it may be allowed for specific drugs or on a case-by-case basis, considering factors like the particular drug, available local resources, and patient characteristics. What Does This Mean? When considering alternatives to directly observed therapy, it may be helpful to assess risks, which may be influenced by the facility, the drug being provided, and individual patients. It may also be beneficial to implement methods of monitoring drug adherence and checking for potential misuse or diversion, such as by reviewing drug administration or supply records. While some alternatives to directly observed therapy may help to reduce time spent administering drug, some potential risks include increased risk of misuse and/or diversion and higher cost of drugs. Additional time may be required initially to allow staff to learn and adapt to the new process. It may be helpful to consider alternatives to directly observed therapy as additional options for patients, allowing patients to have input into what drug they will take based on their needs and concerns.
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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 | 0.011 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".