High Uptake of HIV Testing in Pregnant Women in Ontario, Canada
Bibliographic record
Abstract
In 1999, Ontario implemented a policy to offer HIV counseling and testing to all pregnant women and undertook measures to increase HIV testing. We evaluated the effectiveness of the new policy by examining HIV test uptake, the number of HIV-infected women identified and, in 2002, the HIV rate in women not tested during prenatal care. We analyzed test uptake among women receiving prenatal care from 1999 to 2010. We examined HIV test uptake and HIV rate by year, age and health region. In an anonymous, unlinked study, we determined the HIV rate in pregnant women not tested. Prenatal HIV test uptake in Ontario increased dramatically, from 33% in the first quarter of 1999 to 96% in 2010. Test uptake was highest in younger women but increased in all age groups. All health regions improved and experienced similar test uptake in recent years. The HIV rate among pregnant women tested in 2010 was 0.13/1,000; in Toronto, the rate was 0.28 per 1,000. In the 2002 unlinked study, the HIV rate was 0.62/1,000 among women not tested in pregnancy compared to 0.31/1,000 among tested women. HIV incidence among women who tested more than once was 0.05/1,000 person-years. In response to the new policy in Ontario, prenatal HIV testing uptake improved dramatically among women in all age groups and health regions. A reminder to physicians who had not ordered a prenatal HIV test appeared to be very effective. In 2002, the HIV rate in women who were not tested was twice that of tested women: though 77% of pregnant women had been tested, only 63% of HIV-infected women were tested. HIV testing uptake was estimated at 98% in 2010.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".