The Discursive Construction of Substance Use and Harm Reduction in Canadian Health Policy
Bibliographic record
Abstract
Harm reduction as a philosophy has been widely recognized by healthcare professionals in Canada, yet the topic remains controversial in both political and public discourses. Understanding these discourses will allow health care providers to better respond to political and public concerns, as well as ensuring that services are aligned well with public health needs. This study explored the discursive use of the term “harm reduction” in Canadian health care and nursing policy documents’ contexts by using a Foucauldian framework and Bacchi’s ‘what’s the problem represented to be?’ approach. I propose three discursive themes: self-responsible citizen, evidenced-based practice, and what nurses must do. The findings indicate possibilities for designing favorable and humanistic policies and strategies for people who use substances. This study reveals the problem of how language is an enactment of power over people who use substances and recommends more humanistic policies and empowering language.
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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 | 0.024 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.046 | 0.104 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| 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".