Nurses Supporting Harm Reduction: How Take-Home Naloxone is Conceived in the Context of Neoliberalism
Bibliographic record
Abstract
Introduction – Individuals must be personally invested in their own recovery journey; however, the neoliberal perspective absolves the state of responsibility of this work and makes promotion of health merely an individual action. Naloxone distribution, as a harm reduction strategy, is presented herein as one practice engaged by nurses that demonstrates philosophical tension between neoliberalism and harm reduction. Background Literature – The research literature supporting the provision of take-home naloxone (THN), non-medically administered, is significant and broad. Discussion – The problem with neoliberal discourses of constrained healthcare resources in this case is that without broad availability of naloxone, drug poisonings will continue unchecked. There is an ethical call to nurses to support broad distribution of naloxone regardless of the costs involved. Conclusion – THN is not only a best practice to reduce the harms of substance use, but it is also a political and philosophical act to hand over the control of public health resources to the public.
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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 | 0.041 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.121 |
| Scholarly communication | 0.024 | 0.016 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".