How Can Geographical Information Systems and Spatial Analysis Inform a Response to Prescription Opioid Misuse? A Discussion in the Context of Existing Literature
Bibliographic record
Abstract
The misuse of prescription opioids is a major public health problem in the United States, Canada, Australia and other parts of the developed world. Methods to quantify dimensions of the risk environment in relation to drug usage and law enforcement that are both structural and spatial, draw geography into traditional public health research even though there has been limited attempt to address the prescription opioid misuse problem from a geographic perspective. We discuss how geographic technologies can be utilized to study the landscape of prescription opioids and similar drugs, and target appropriate health services interventions. We use examples drawn from various jurisdictions to present our case and highlight through these examples how a geospatial perspective can help support research on prescription opioid misuse. The prescription drug misuse landscape can be studied through examination of the domains of demand, supply, harms and harm reduction. We discuss how each of these domains can benefit from a local geographic perspective, and subsequent geographic exploration and analyses.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".