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Record W4312018425 · doi:10.1089/jchc.21.08.0079

A Cross-Sectional, Retrospective Evaluation of Opt-Out Sexually Transmitted Infection Screening at Admission in a Short-Term Correctional Facility in Alberta, Canada

2022· article· en· W4312018425 on OpenAlexaffabout
Alexandra Reekie, Jennifer Gratrix, Petra Smyczek, Dan Woods, Katherine Poshtar, Keith Courtney, Rabia Ahmed

Bibliographic record

VenueJournal of Correctional Health Care · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsAlberta Health ServicesUniversity of AlbertaAlberta HealthUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineGonorrheaChlamydiaSyphilisCross-sectional studyPopulationRetrospective cohort studySexually transmitted diseaseDemographyAsymptomaticFamily medicineHuman immunodeficiency virus (HIV)Environmental healthInternal medicineImmunology

Abstract

fetched live from OpenAlex

Incarcerated populations experience higher rates of sexually transmitted infections (STIs) than the general population, alongside inconsistent testing strategies. In response, universal opt-out STIs (chlamydia, gonorrhea, syphilis, and HIV) screening was implemented at admission in a short-term correctional facility in Alberta, Canada, for individuals ≤35 years. A cross-sectional, retrospective evaluation of testing outcomes between March 2018 and February 2020 was completed. Descriptive statistics were used to stratify STIs by gender, age group, and date for univariate analysis. Despite low uptake (31.2%), opt-out screening resulted in high positivity rates (14.9%, 10.8%, 29.5%, and 0.3%, respectively) and treatment completion (93.7%) while capturing a high proportion (52.6%) of asymptomatic cases. Opt-out screening at admission is feasible and can improve STI testing in high-risk individuals experiencing incarceration in Canada.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.390
Teacher spread0.342 · 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.

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

Citations6
Published2022
Admission routes2
Has abstractyes

Explore more

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