Integrating harm reduction into acute care: A single center's experience
Bibliographic record
Abstract
Objective: Injection drug use (IDU) is prevalent in North America and is associated with presentations with infective endocarditis. Supporting patients who present with infective endocarditis related to IDU through harm reduction, a pragmatic approach to reduce secondary harms of a health behavior, helps address the underlying IDU. We share a case exemplar of how one acute care facility integrated harm-reduction practices into daily patient care. Methods: We took a 3-stage approach to integrate harm-reduction practices into daily patient care. In stage 1, we raised awareness and knowledge of harm reduction through education. In stage 2, we provided explicit support for harm reduction. In stage 3, we provided tangible tools to support harm reduction. Results: More than 300 staff attended education sessions and reported increased knowledge related to substances, harm reduction, and engaging patients who use substances in conversations. Staff requested the hospital explicitly support harm reduction, which led to stage 2. The creation of a harm-reduction philosophy statement provided permission to engage in harm-reduction practices. Stage 3 included the creation of a harm-reduction supply distribution program and consultations with Addictions Medicine and treatment programs. The implementation of harm-reduction supply distribution was successful and is being spread across the facility. Conclusions: Engaging in harm-reduction practices within an acute care facility is possible through a multistage process focused on education, explicit support, and tangible tools. Spreading harm-reduction integration and working with patients who used substances to evaluate effectiveness are key next steps.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".