Harm reduction in hospitals: is it time?
Bibliographic record
Abstract
Among persons who inject drugs (IDU), illicit drug use often occurs in hospitals and contributes to patient expulsion and/or high rates of leaving against medical advice (AMA) when withdrawal is inadequately managed. Resultant disruptions in medical care may increase the likelihood of several harms including drug resistance to antibiotics as well as costly readmissions and increased patient morbidity. In this context, there remains a clear need for the evaluation of harm reduction strategies versus abstinence-based strategies with respect to addressing ongoing issues related to substance use among addicted hospitalized patients. While hospitalization can be used to stabilize addicted patients as they recover from their acute illness and help them to achieve abstinence, patients unable to maintain abstinence should not be penalized for failing to do so at the expense of their health. This article describes harm reduction activities within hospitals and areas for future investigation.
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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 | 0.020 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".