Examining the public discourse around the closures of safe injection sites: thematic analysis (Preprint)
Bibliographic record
Abstract
BACKGROUND Safe injection sites (SIS) are evidence-based harm reduction facilities that offer supervised environments for drug use to prevent overdose deaths and infectious disease transmission. Despite their demonstrated effectiveness, the Ontario government announced the closure of several SISs by March 2025, citing public safety concerns, including their proximity to schools. This policy shift sparked widespread public discourse, especially on social media platforms like Reddit, where anonymity enables open discussion on controversial topics. OBJECTIVE This study aims to examine public discourse of SIS closures in Ontario by analyzing Reddit discussions, providing insights into community sentiments, thematic discourse, and the political and social factors shaping attitudes toward harm reduction policies. METHODS Reddit data were collected between August and December 2024, focusing on six subreddits from affected Ontario cities. A total of 771 comments across 28 threads were retrieved using keywords related to SIS and opioid use. After preprocessing, 549 relevant comments were retained. An inductive thematic analysis following Braun & Clarke’s framework was conducted to identify discourse patterns. Descriptive statistics, upvote analysis, and Spearman’s correlation tests were employed to explore theme prevalence, user engagement, and associations between comment length and agreement. RESULTS Seven primary themes emerged: (1) support for SIS closures, (2) support for SISs, (3) criticism of government policies, (4) housing and homelessness, (5) alcohol policy comparisons, (6) child exposure concerns, and (7) personal attacks on Premier Doug Ford. Emerging themes addressed broader systemic issues like healthcare privatization. The most prevalent theme (31%) criticized the government’s lack of integrated support services for addiction recovery. A weak but statistically significant correlation (rₛ = 0.132, p < .001) was found between comment length and upvotes. Although support for closures was visible (25%), many users expressed concern over the absence of viable alternatives, inadequate mental health services, and politicization of public health decisions. CONCLUSIONS The analysis reveals polarized public discourse on SIS closures in Ontario. While some Reddit users endorsed the government’s decision, citing safety concerns, many opposed the closures due to the lack of evidence-based policy and insufficient alternative support systems. The discourse reflects broader societal tensions between public safety and harm reduction, highlighting the need for comprehensive addiction support strategies. This study demonstrates the value of social media data in capturing real-time public reactions to policy changes and offers insights for future health communication and policymaking.
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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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".