Lessons Learned After Losing my Brother to an Overdose: A Call to Action for Nurse Leaders
Bibliographic record
Abstract
The current overdose epidemic we are facing in Canada and internationally calls on nursing leaders to prioritize holistic and compassionate care for people who use drugs (PWUD) and their families. Nurses are well positioned to provide person-centred care and advocate with and for this population. To do so requires an examination of one's personal values and beliefs surrounding drugs and the people who use them. As a nurse leader, I was forced to confront my views about illicit drug use following the untimely death of my brother Brad from overdose. This paper chronicles my personal experience with his death and subsequent journey into advocacy for drug policy reform amidst an emerging overdose crisis. This short paper is written from my personal perspective, and informed by both personal and professional experiences in drug policy reform. It addresses strategies for challenging stigma and opportunities for partnering with PWUD through engagement in harm reduction.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.017 | 0.037 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".