MétaCan
Menu
← Back to cohort
Record W2744686964 · doi:10.3138/cjccj.2017.0001.r2

Take-Home Naloxone Kit Distribution: A Pilot Project Involving People Who Use Drugs and Who Are Newly Released from a Correctional Facility

2017· article· en· W2744686964 on OpenAlexaffvenueabout
Em M. Pijl, Stacey Bourque, Madison Martens, Ashley Cherniwchan

Bibliographic record

VenueCanadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénale · 2017
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsUniversity of Lethbridge
Fundersnot available
Keywords(+)-NaloxoneHarm reductionOpioid overdoseMedicineHarmPopulationDistribution (mathematics)DrugMedical emergencyEnvironmental healthOpioidPharmacologyPublic healthNursingPsychologySocial psychology

Abstract

fetched live from OpenAlex

Due to a recent increase in opioid overdoses in Canada, new harm reduction strategies are emerging. One of these strategies is take-home naloxone (THN) kits for individuals who use drugs being released from correctional facilities. Given the efficacy of naloxone for overdose reversal, the distribution of this medication to drug users upon release from incarceration has the potential for an impact on the incidence of drug-related death among this population. This group is at risk of overdose post-release due to lowered opioid tolerance and drugs of unknown strength. In this article, we report on the findings of a THN kit program for newly released inmates. This pilot project embodied a strong collaborative spirit between a provincial corrections facility and a not-for-profit harm reduction agency. Due to the success of this pilot project, this program was rolled out provincially in correctional centres across Alberta, overseen by the provincial health authority.

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)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.334

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0110.003
Scholarly communication0.0020.001
Open science0.0030.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.099
GPT teacher head0.302
Teacher spread0.203 · 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 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

Citations4
Published2017
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénale→Same topicOpioid Use Disorder Treatment→French-language works237,207→