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Record W4389791297 · doi:10.1111/dar.13796

Three noteworthy idiosyncrasies related to Canada's opioid‐death crisis, and implications for public health‐oriented interventions

2023· article· en· W4389791297 on OpenAlexafffundabout
Benedikt Fischer, Tessa Robinson, Didier Jutras‐Aswad

Bibliographic record

VenueDrug and Alcohol Review · 2023
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de MontréalImpactSimon Fraser UniversityMcMaster UniversityUniversity of TorontoUniversity of the Fraser Valley
FundersCanadian Institutes of Health Research
KeywordsPsychological interventionPublic healthMedicineIntervention (counseling)Drug overdoseOpioidOpioid overdoseFentanylMedical prescriptionDrugPsychiatryEnvironmental healthPoison controlPharmacology(+)-NaloxoneNursing

Abstract

fetched live from OpenAlex

Abstract Canada has been experiencing a prolonged public health‐crisis of high rates of overdose deaths caused by exceptionally potent/toxic, illicit opioid use. While many key features of this drug death epidemic are well‐documented, several idiosyncratic aspects with relevance for public health‐oriented interventions are not adequately recognised. These include: (i) the discrepant opioid patterns pan‐Canada, with large majorities of opioid deaths caused by illicit fentanyl drugs in Western, but not Eastern regions where prescription‐type opioid prevail; (ii) the environments of overdose deaths, where vast majorities occur in ‘residential’ or other shelter‐type settings, presenting barriers for emergency interventions rather than health protection; and (iii) shifting drug use modes, where now majorities of overdose deaths are associated with drug ‘inhalation’ (instead of ‘injection’) in contexts of potent/toxic drug supply. We briefly describe these factors and related implications for intervention programming towards an improved response to the drug death‐crisis.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.484
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.070
GPT teacher head0.374
Teacher spread0.304 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations13
Published2023
Admission routes3
Has abstractyes

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