Public preferences for safe consumption sites for opioid use: A discrete choice experiment
Bibliographic record
Abstract
BACKGROUND: Safe consumption sites provide people who use drugs with medical supervision and sterile paraphernalia for drug use. Although the presence of sites in neighborhoods can be controversial, few studies have assessed the preferences of individuals for attributes of safe consumption sites. METHODS: A discrete choice experiment was conducted to assess public preferences for safe consumption sites. Logit and mixed logit models were used to analyze data. Participants were recruited from Conjointly.com using a sample of the general population in Canada. The sample included adults only, was split approximately evenly by gender, and reflects census data for household income and geographic area. Attributes included: cost of the site to the healthcare system; effectiveness of the site in reducing overdose death; financial compensation to residents if a site opens in their neighborhood; if the site is located in the respondent's neighborhood; and if the site reduces improperly discarded needles. RESULTS: The sample consisted of 203 respondents. Respondents had negative preferences for sites that increased cost to the healthcare system. Conversely, they had positive preferences for sites that would reduce fatal overdoses, that could reduce improperly discarded needles, and sites that provided compensation to those impacted by the establishment of sites. CONCLUSIONS: Findings suggest that there exist a set of attributes that influence respondents' preferences for safe consumption sites. By considering these attributes when designing sites and developing messaging for sites, decision-makers may develop sites that are potentially less controversial.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".