Substance Use in Saskatchewan: Calibration and Parameterization for a Computational Epidemiology Approach to Substance Use Research
Bibliographic record
Abstract
Background: Opioids have greatly affected the health and wellbeing of people around the world, in a multitude of ways. In Saskatchewan, rates of opioid related overdoses and overdose deaths have been increasing over time. Implementation of treatment, prevention, and harm reduction services as a continuum of care is associated with noticeable levels of change in the numbers of drug toxicity deaths in Saskatchewan and around the world. However, the stigmatization of substance use and people who use drugs makes it very difficult for people to willingly access these services. This work primarily aims to determine what service changes or policy changes would most positively impact harm reduction practices and the reduction of drug toxicity deaths of people who use drugs in Saskatchewan. Methodology: Using mixed methodology of environmental scans and statistical analysis of publicly available data, this thesis produces a foundation of knowledge surrounding substance use in Saskatchewan that will be used in the creation of models by the Computational Epidemiology and Public Health Informatics Laboratory (CEPHIL). Findings: Rates of hospital visits, EMS response to overdose, and acute drug toxicity death have been increasing in Saskatchewan for decades; most prominently in the past 5 years. Conversely, rates of criminal offences have been decreasing while rates of incarceration have ben increasing. Most incarcerated people are awaiting remand, pointing to potential roadblocks with the clearance of criminal case files and, thereby, artificially reducing the rates of criminal convictions. In the absence of additional public data, tools such as computational models can be used to inform about the outcomes of our current systems. These tools may be beneficial in showing not only how these rates will continue to change over time, but also the burdens that attributable public expenses will place on the change in quality and availability of public services. Conclusion: Further exploration is needed to determine more precise changes in rates of hospital visits, EMS response to overdose, acute drug toxicity death, wastewater levels of substances of concern, criminal convictions, and incarceration. Limited publicly available data restricts specific conclusions that can be made about the current rates. Further, it is possible that lack of availability of this information contributes to misinformation in public sectors when discussing the impacts of substance use within a community. Improvements in data collection, analysis, and publication are needed to prevent misinformation and to improve research that can be done in the field of substance use. In the absence of available data, computational epidemiology methods may offer support in understanding the ways that substance use impacts the rate changes, the ways that any of these rates impact another, and the ways that innovation in policy can reduce these rates and their attributable public costs. Further development of computational models will be supported through the findings in this thesis.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".