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Record W4214912378 · doi:10.1186/s40352-022-00174-w

Buprenorphine/naloxone access for people with opioid use disorder in correctional facilities: taking steps to support knowledge translation

2022· article· en· W4214912378 on OpenAlexafffund
Marina Sadik, Erin Beaulieu, Claire Bodkin, Lori Kiefer, Dale Guenter, Patsy W. P. Lee, Fiona G. Kouyoumdjian

Bibliographic record

VenueHealth & Justice · 2022
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsPublic Health OntarioUniversity of TorontoMcMaster University
FundersCanadian Institutes of Health Research
KeywordsBuprenorphine(+)-NaloxoneOpioid use disorderOpioid overdoseMedicineKnowledge translationOpioidPsychiatryMedical emergencyKnowledge managementInternal medicineComputer science

Abstract

fetched live from OpenAlex

People with opioid use disorders are overrepresented in correctional facilities, and are at high risk of opioid overdose. Despite the fact that buprenorphine/naloxone is the first line treatment for people with opioid use disorder, there are often institutional, clinical, and logistical barriers to buprenorphine/naloxone initiation in correctional facilities. Guided by the knowledge-to-action framework, this knowledge translation project focused on synthesizing knowledge and developing a tool for buprenorphine/naloxone initiation that was tailored to correctional facilities, including jails. This information and tool can be used to support buprenorphine/naloxone access for people in correctional facilities, in parallel with other efforts to address barriers to treatment initiation in correctional facilities.

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)0.088
metaresearch head score (Gemma)0.133
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.088
Threshold uncertainty score0.468

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0880.133
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0060.004
Scholarly communication0.0100.011
Open science0.0040.014
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0110.002

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 head0.082
GPT teacher head0.373
Teacher spread0.291 · 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 designNot applicable
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

Citations3
Published2022
Admission routes2
Has abstractyes

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