The need for integrated public health surveillance to address sexually transmitted and blood-borne syndemics
Bibliographic record
Abstract
A national approach to addressing sexually transmitted and blood-borne infections (STBBIs) was recently articulated in the Public Health Agency of Canada's new A Pan-Canadian Framework for Action: Reducing the health impact of sexually transmitted and blood-borne infections in Canada by 2030.This Framework promotes an integrated approach, with a focus on the key populations that are affected by overlapping epidemics (i.e., syndemics).We advance the idea that integrating surveillance would be helpful in characterizing and understanding the populations, locations, risk behaviours and other drivers that contribute to STBBI syndemics.The creation of matched or linked data systems that would allow routine reporting of integrated data is challenged by the technical barriers of integrating data silos as well as by the privacy and ethical considerations of merging sensitive individual-level data.Lessons can be learned from jurisdictions where an improved understanding of syndemics, through integrated STBBI surveillance, has led to more efficient and effective operational, program and policy decisions.Emerging enablers include the development of data standards and guidelines, investment in resources to overcome technical challenges and community engagement to support the ethical and non-stigmatizing use of integrated data.The Framework's call to action offers an opportunity for national discussion on priorities and resources needed to advance STBBI syndemic surveillance for local, regional and national reporting in Canada.
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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 | 0.049 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".