P138 Evaluating Opt-Out STI Testing at Admission within a Short-Term Correctional Facility Located in Alberta, Canada
Bibliographic record
Abstract
Background Incarcerated Canadians entering into correctional facilities experience higher rates of sexually transmitted infections (STI) than the general population. Yet, testing across correctional facilities remains inconsistent and symptom- or risk-based. Thus, STI prevalence is likely underestimated, and the number of undiagnosed individuals remains high. In response, Alberta implemented universal opt-out STI (chlamydia, gonorrhea, syphilis)/HIV screening at admission in a short-term correctional facility for all individuals ≤ 35 years. This study evaluates opt-out screening at admission to provide Canadian-specific data as a comparative to current opt-in (symptom/risk-based) testing strategies. Specific outcomes were determined: (1) the uptake of opt-out screening, (2) reasons for opt-out screening non-completion, (3) STI/HIV positivity rates and (4) treatment completion rates. Methods A cross-sectional, retrospective analysis of opt-out screening outcomes between March 2018 and February 2020 was completed. Test data was extracted from admission line-lists, laboratory data and communicable disease treatment data. Descriptive statistics were used to stratify test data by STI, gender, age group, and date for univariate analysis. Results Opt-out screening was offered to the majority (96.7%) of admissions, while opt-out screening non-completion was largely attributed to patient decline (67.4%). Despite low testing uptake (31.2%), opt-out screening achieved high positivity rates (14.9% chlamydia, 10.8% gonorrhea, 29.5% syphilis and 0.3% HIV) and treatment completion rates (94.1% overall). Furthermore, 52.6% of opt-out cases were asymptomatic. Conclusions Although limited by retrospective analysis, opt-out screening at admission is a feasible strategy to increase STI/HIV testing and detect asymptomatic cases in a correctional setting. Communicative strategies within the facility will be critical to increase testing uptake among patients. Intensified screening and subsequent treatment can impact community transmission while reducing healthcare accessibility barriers for a vulnerable population. Further, implementing opt-out STI/HIV screening in other high-risk settings, such as mental health and addictions, can be an effective case-finding strategy for outbreak management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".