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Record W3013475638 · doi:10.1186/s12954-019-0336-0

Evaluating networked drug checking services in Toronto, Ontario: study protocol and rationale

2020· article· en· W3013475638 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHarm Reduction Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsToronto Public HealthCentre for Addiction and Mental HealthUniversity of TorontoUniversity of British ColumbiaPublic Health OntarioRegent Park Community Health CentreSt. Michael's Hospital
FundersCanadian Institutes of Health ResearchHealth CanadaNational Institute on Drug AbuseOntario Ministry of Research, Innovation and ScienceSt. Michael's Hospital Foundation
KeywordsHarm reductionPublic healthMedicineOpioid overdoseEnvironmental healthMedical emergencyBusinessNursingOpioid

Abstract

fetched live from OpenAlex

BACKGROUND: The increasing incidence of fatal opioid overdose is a public health crisis in Canada. Given growing consensus that this crisis is related to the presence of highly potent opioid adulterants (e.g., fentanyl) in the unregulated drug supply, drug checking services (DCS) have emerged as part of a comprehensive approach to overdose prevention. In Canada's largest city, Toronto, a network of DCS launched in 2019 to prevent overdose and overdose-related risk behaviors. This network employs mass spectrometry technologies, with intake sites co-located with supervised consumption services (SCS) at three frontline harm reduction agencies. The protocol and rationale for assessing the impact of this multi-site DCS network in Toronto is described herein. The aims of this study are to (1) evaluate the impact of DCS access on changes in and factors influencing overdose and related risk behaviors, (2) investigate the perceived capacity of DCS to prevent overdose, and (3) identify composition (qualitative and quantitative) trends in Toronto's unregulated drug supply. METHODS: We will use a parallel-mixed-methods design with complementary data sources (including data from chemical analysis of drug samples, quantitative intake and post-test surveys, SCS, coroners, paramedic services, and qualitative interviews), followed by a meta-inference process wherein results from analyses are synthesized. RESULTS: Whereas most DCS globally target "recreational drug users," in Toronto, this networked DCS will primarily target marginalized people who use drugs accessing frontline services, many of whom use drugs regularly and by injection. This evolution in the application of DCS poses important questions that have not yet been explored, including optimal service delivery models and technologies, as well as unique barriers for this population. Increasing information on the unregulated drug supply may modify the risk environment for this population of people who use drugs. CONCLUSIONS: This study addresses evidence gaps on the emerging continuum of overdose prevention responses and will generate critical evidence on a novel approach to reducing the ongoing high incidence of drug-related morbidity and mortality in Canada and elsewhere.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.056
GPT teacher head0.382
Teacher spread0.327 · 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