MétaCan
Menu
Back to cohort
Record W7006060729

Substance Use in Saskatchewan: Calibration and Parameterization for a Computational Epidemiology Approach to Substance Use Research

2025· article· en· W7006060729 on OpenAlexfundaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2025
Typearticle
Languageen
FieldMathematics
TopicCensus and Population Estimation
Canadian institutionsnot available
FundersHealth CanadaPublic Health Agency of CanadaGovernment of CanadaU.S. Department of JusticePublic Health AgencyUniversity of Saskatchewan
KeywordsHarm reductionHarmPublic healthEpidemiologyDrug overdoseInformaticsSubstance abusePoison controlHealth care
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.482
Threshold uncertainty score0.969

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0030.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.113
GPT teacher head0.297
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueUniversity Library (University of Saskatchewan)Same topicCensus and Population EstimationFrench-language works237,207