That Look That Makes You Not Really Want to be There: How Neoliberalism and the War on Drugs Compromise Nursing Care of People Who Use Substances
Bibliographic record
Abstract
Abstract Research conducted in 2016 explored the health care experiences of people who use illicit opioids in small Ontario urban and rural communities. Perspectives of participants who used opioids and of nurse participants were interpreted using Friere’s critical social theory framework to explore sociopolitical, economic and ideological influences. Findings describe pervasive experiences of stigma, discrimination and inappropriate care. Exploration of why such negative experiences with nursing care might be so pervasive led to a consideration of the context of health care systems and in particular of the influences of neoliberalism and the impact of the global War on Drugs. Mitigation strategies to support contextualized nursing practice are outlined. Nurses are called upon to actively resist the pressures of these political forces by advocating for policy change including decriminalization.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.046 | 0.100 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".