A qualitative study of the perceived effects of blue lights in washrooms on people who use injection drugs
Bibliographic record
Abstract
BACKGROUND: Blue lights are sometimes placed in public washrooms to discourage injection drug use. Their effectiveness has been questioned and concerns raised that they are harmful but formal research on the issue is limited to a single study. We gathered perceptions of people who use injection drugs on the effects of blue lights with the aim of informing harm reduction practice. METHODS: We interviewed 18 people in two Canadian cities who currently or previously used injection drugs to better understand their perceptions of the rationale for and consequences of blue lights in public washrooms. RESULTS: Participants described a preference for private places to use injection drugs, but explained that the need for an immediate solution would often override other considerations. While public washrooms were in many cases not preferred, their accessibility and relative privacy appear to make them reasonable compromises in situations involving urgent injecting. Participants understood the aim of blue lights to be to deter drug use. The majority had attempted to inject in a blue-lit washroom. While there was general agreement that blue lights do make injecting more difficult, a small number of participants were entirely undeterred by them, and half would use a blue-lit washroom if they needed somewhere to inject urgently. Participants perceived that, by making veins less visible, blue lights make injecting more dangerous. By dispersing public injection drug use to places where it is more visible, they also make it more stigmatizing. Despite recognizing these harms, more than half of the participants were not opposed to the continued use of blue lights. CONCLUSIONS: Blue lights are unlikely to deter injection drugs use in public washrooms, and may increase drug use-related harms. Despite recognizing these negative effects, people who use injection drugs may be reluctant to advocate against their use. We attempt to reconcile this apparent contradiction by interpreting blue lights as a form of symbolic violence and suggest a parallel with other emancipatory movements for inspiration in advocating against this and other oppressive interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".