Three infections, one fight: Protocol for an implementation study to map needle prevalence and implement HIV, syphilis, and hepatitis C prevention interventions in Regina, Saskatchewan
Bibliographic record
Abstract
Introduction: Saskatchewan is facing a public health crisis driven by high rates of HIV, syphilis, and hepatitis C (HCV) infections, particularly among people who use drugs (PWUD). Injection drug use is a major contributor to these syndemic infections, exacerbated by structural barriers such as stigma, poverty, and limited culturally safe healthcare. Innovative, community-informed approaches are urgently needed to improve prevention, testing, and linkage to care. Methods and analysis: This study will implement a rapid assessment and response system in Regina, Saskatchewan, Canada integrating geospatial mapping of community needle prevalence with pop-up interventions. Needle hotspot maps will be used to guide the deployment of community-based pop-up events offering point-of-care testing for HIV, syphilis, and HCV, alongside education on pre- and post-exposure prophylaxis (PrEP and PEP). A convergent participatory mixed-methods design will be used to evaluate feasibility, acceptability, and effectiveness, guided by the Reach, Effectiveness, Adoption, Implementation, and Maintainence (RE-AIM) framework. Quantitative data will assess changes in knowledge of PrEP and PEP, satisfaction with the intervention, and report new diagnoses and participant demographics descriptively. A qualitative sub-study will include 30 participants and will explore experiences with the intervention, barriers to care, and perceptions of service delivery. Ethics and dissemination: Ethical approval has been obtained from the Research Ethics Board of the Saskatchewan Health Authority (#24-91). Findings will be disseminated through peer-reviewed publications, conference presentations, and community reporting. This study may provide a model of community-based, geospatial testing and education that could be upscaled and adapted elsewhere. Registration: Open Science Framework https://doi.org/10.17605/OSF.IO/HVK3B
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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 | 0.064 | 0.043 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.125 | 0.031 |
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".