Spatial analysis of discarded needles and dropbox locations in Calgary, Canada
Bibliographic record
Abstract
Concomitant with the rise in the number of people who inject drugs has been an increase in unsafely discarded needles and injection debris. While the scholarly literature indicates that harm reduction programs reduce needle debris, the news media often report otherwise. Using geographic information systems (GIS) software ArcGIS Desktop 10.8 (Esri 2020), we analyzed geospatial data pertaining to needle debris in Calgary (Canada), correlating debris with available needle dropboxes, outreach education, overdoses, and changes over the COVID pandemic. Needle debris was most dense in two central neighbourhoods: Beltline and Downtown Commercial Core. The city’s central neighbourhoods contributed to 83% of all needle discards, which accounted for 73% of discrete locations. Additionally, 51% of discarded needles were collected from the Beltline (40%) and Downtown Commercial Core (11%) neighbourhoods, accounting for 85% of clusters and 71% of hotspots. Overdoses were positively correlated with needle debris. COVID-19 pandemic restrictions were linked to a spike in the number of discards. Needle debris is a complex social, environmental and public health issue that requires a multifaceted approach. GIS mapping is a powerful tool that can locate hotspots so that resources can be deployed.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".