Intravenous Drug Use in the Hospital Setting
Bibliographic record
Abstract
ABSTRACT: People who inject drugs are likely to end up admitted to a hospital due to complications associated with substance use. While in hospital, many of these patients will continue the self-administration of nonprescribed drugs. When implemented without a harm reduction approach, self-administration can lead to an increase in the acquisition of infectious diseases, injection-related infections, and fatal and nonfatal overdoses. Often, administrators and providers use punitive approaches to manage this behavior among patients and providers. This abstinence-based approach has, and continues to, disproportionally impact structurally vulnerable communities. To mobilize the Truth and Reconciliation Commission's Calls to Action, Canadian hospitals must respond transparently and urgently to Indigenous peoples, patients, and communities. For example, First Nations, Inuit, and Metis people and communities living in Canada are significantly affected by the opioid epidemic, which can be traced back to the legacy of and continued colonialism and systemic discrimination in health care. Colonial policies and systems manifest as Indigenous populations experiencing a high prevalence of socioeconomic disadvantage and poor access to quality health and social services, on- and off-reserve. Clinicians must understand and receive cultural safety training to adequately care for Indigenous patient populations, as well as other structurally vulnerable populations. Additionally, Canadian hospitals should acknowledge and measure intravenous drug use in their organizations and take a harm reduction approach to mitigate associated adverse outcomes. Finally, hospitals should work with academic institutions to train, recruit, and retain Indigenous clinicians from diverse sociocultural backgrounds.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".