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Record W2995453886 · doi:10.1093/pubmed/fdz162

Applying principles of injury and infectious disease control to the opioid mortality epidemic in North America: critical intervention gaps

2019· article· en· W2995453886 on OpenAlexafffundabout
Benedikt Fischer, Michelle Pang, Mark Tyndall

Bibliographic record

VenueJournal of Public Health · 2019
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsUniversity of British ColumbiaCentre for Addiction and Mental HealthBC Centre for Disease ControlUniversity of TorontoSimon Fraser University
FundersFaculty of Medical and Health Sciences, University of AucklandCanadian Institutes of Health Research
KeywordsPsychological interventionPublic healthMedicineOpioid overdoseEnvironmental healthIntervention (counseling)Public health interventionsOpioidPoison controlDiseaseInjury preventionIntensive care medicinePsychiatry(+)-NaloxoneNursing

Abstract

fetched live from OpenAlex

North America has been experiencing an acute and unprecedented public health crisis involving excessive and increasing levels of opioid-related overdose mortality. In the present commentary, we examine current interventions (as existent mainly in Canada) to date and compare them against established intervention frameworks and practices in other areas of public health, specifically injury and infectious disease control. We observe that current interventions focusing on opioid drug safety or exposure-specifically those that focus on distinctly potent and toxic opioid products driving major increases in overdose mortality-may be considered the equivalent of 'agent-' or 'vector'-based interventions. Such interventions have been largely neglected in favor of 'host' (e.g., drug user-oriented) or 'environmental' measures among strategies to reduce opioid-related overdose, likely contributing to the limited efficacy of current measures. We explore potential reasons, implications and remedies for these gaps in the overall public health strategy employed towards improved interventions to reduce opioid-related health harms.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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)0.0000.000

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 head0.037
GPT teacher head0.368
Teacher spread0.331 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations12
Published2019
Admission routes3
Has abstractyes

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