Working near a supervised injection facility: A qualitative study of perspectives of firefighter‐emergency medical responders
Bibliographic record
Abstract
BACKGROUND: While firefighter-emergency medical responders (FF-EMR) are important stakeholders in cities considering the implementation of a supervised injection facility (SIF), there is little information on perspectives of first responders who serve these communities. The aim of the present study was to identify FF-EMR perspectives on working near a SIF. METHODS: FF-EMRs from Vancouver Fire and Rescue Services completed an online survey that queried participant perspectives on working near a SIF. RESULTS: Four main themes were identified: positive effects, negative effects, duration of assignment, and sense of duty. Similar percentages of first responders reported positive (22.2%) and negative aspects (25.9%) of working near the SIF, while some (18.5%) indicated preference for a short-term assignment to the SIF area. FF-EMRs most commonly described a sense of duty (35.2%). CONCLUSIONS: To our knowledge, our study is the first to identify FF-EMR perspectives related to work near a SIF. Perspectives and concerns of first responders should be considered in policy debates about implementation of new SIFs to guarantee an adequately-prepared first responder workforce.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".