Three infections, one fight: an implementation study to map needle prevalence and evaluate HIV, syphilis and hepatitis C prevention interventions in Regina, Saskatchewan – a protocol
Bibliographic record
Abstract
INTRODUCTION: Saskatchewan is facing a public health crisis driven by high rates of HIV, syphilis and hepatitis C virus (HCV) infections, particularly among people who use drugs. 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-exposure prophylaxis (PrEP) and postexposure prophylaxis (PEP). A convergent participatory mixed-methods design will be used to evaluate feasibility, acceptability and effectiveness, guided by the Reach, Effectiveness, Adoption, Implementation and Maintenance 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 substudy 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 scaled up 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 | 0.028 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".