Increasing public support for supervised injection facilities in Ontario, Canada
Bibliographic record
Abstract
AIM: To determine the level and changes in public opinion between 2003 and 2009 among adult Canadians about implementation of supervised injection facilities (SIFs) in Canada. DESIGN: Population-based, telephone survey data collected in 2003 and 2009 were analysed to identify strong, weak, and intermediate support for SIFs. SETTING: Ontario, Canada PARTICIPANTS: Representative samples of adults aged 18 years and over. MEASUREMENTS: Analyses of the agreement with implementation of SIFs in relation to four individual SIF goals and a composite measure. FINDINGS: The final sample sizes for 2003 and 2009 were 1212 and 968, respectively. Between 2003 and 2009, there were increases in the proportion of participants who strongly agreed with implementing SIFs to: reduce neighbourhood problems (0.309 versus 0.556, respectively); increase contact of people who use drugs with health and social workers (0.257 versus 0.479, respectively); reduce overdose deaths or infectious disease among people who use drugs (0.269 versus 0.482, respectively); and encourage safer drug injection (0.213 versus 0.310, respectively). Analyses using a composite measure of agreement across goals showed that 0.776 of participants had mixed opinions about SIFs in 2003, compared with only 0.616 in 2009. There was little change among those who strongly disagreed with all SIF goals (0.091 versus 0.113 in 2003 and 2009, respectively). CONCLUSIONS: Support for implementation of supervised injection facilities in Ontario, Canada increased between 2003 and 2009, but at both time-points a majority still held mixed opinions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".