How Do Nursing Organizations Measure Up on Harm Reduction? An Environmental Scan
Bibliographic record
Abstract
BACKGROUND: In the past five years, we have seen a rapid expansion of harm reduction approaches, programs, and policies in Canada. To keep up with the changing policy landscape, a number of Canadian researchers have undertaken projects that seek to analyze policy documents published by provincial and territorial governments. Building on this important body of work, we undertook a similar analysis using documents published by nursing organizations. PURPOSE: To present key findings and propose ways that nursing organizations can strengthen their position on harm reduction. METHODS: We conducted an environmental scan with a two-part analysis. To complete the first part, we used the 17 quality indicators. To complete the second part, we analyzed the documents for specific harm reduction interventions. RESULTS: A total of 39 documents were collected across 76 nursing organizations. The majority of the documents were press or public statements (n = 22), and the most frequently mentioned intervention was supervised injection services (n = 31). On average, documents met 5.6 quality indicators. Documents scored highest on indicator 12 (discuss low-threshold approaches to service provision) and lowest on indicator 3 (acknowledge that not all substance use is problematic). CONCLUSIONS: Six areas were identified to strengthen nursing organizations' position on 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 | 0.051 | 0.182 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.040 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".