Combining Data Sources to Monitor the HIV Epidemic in Canada
Bibliographic record
Abstract
This article describes the methods, results and future perspectives of four information sources used to monitor the HIV epidemic in Canada: AIDS case surveillance, HIV case surveillance, HIV sentinel serosurveillance, and behavioral surveillance. Synthesizing data from these multiple sources provides a more comprehensive picture of the HIV epidemic than any one source alone could provide. In Canada, there has been a shift over time from an epidemic dominated by men who have sex with men to one where more than half of new infections are attributed to other groups, such as injection drug users and non-injecting heterosexuals. The available evidence also suggests increasing HIV infections among Aboriginal persons and among women. Surveillance data have been used in Canada to guide prevention and care programs and to formulate policy. In particular, these data have been used to support the development of an HIV testing program in pregnancy, to re-direct community work toward injection drug users and the young, and to demonstrate the effectiveness of new treatments for HIV. The main challenge now is to continue to improve the monitoring of the shifting HIV epidemic with more accurate data and to use the resulting information to inform appropriate prevention and care responses.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.023 | 0.036 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".