High prevalence of unintended pregnancies in HIV‐positive women of reproductive age in Ontario, Canada: a retrospective study
Bibliographic record
Abstract
OBJECTIVES: There is speculation, but there are few data, on the high rates of unintended pregnancies in HIV-positive women. We investigated rates and correlates of unintended pregnancies among HIV-positive women of reproductive age. METHODS: A cross-sectional study was conducted with recruitment stratified to match the geographical distribution of HIV-positive women of reproductive age (18-52 years) living in Ontario, Canada. Women, recruited from 38 sites between October 2007 and April 2009, were invited to complete a 189-item self-administered survey. This analysis focused on questions relating to pregnancy and whether the last pregnancy was intended. Logistic regression models were fitted to calculate unadjusted and adjusted odds ratios of correlates of unintended pregnancies occurring after HIV diagnosis. Happiness with unintended pregnancies was also assessed. RESULTS: The median age at the time of the survey of the 416 participating HIV-positive women who were previously pregnant (53% before and 47% after HIV diagnosis) was 38 years [interquartile range (IQR) 33-44 years] and their last pregnancy was a median of 8 years (IQR 3-14 years) prior to the survey (n=283). Fifty-nine per cent were born outside Canada and 47% were of African ethnicity. Of the 416, 56% [95% confidence interval (CI) 51-61%] identified that their last pregnancy was unintended (57% before and 54% after HIV diagnosis). In the multivariable model, significant correlates of unintended pregnancy after HIV diagnosis were: marital status (P=0.01) and never having given birth (P=0.01). Women were less happy if their pregnancy was unintended (P<0.01). CONCLUSIONS: The prevalence of unintended pregnancy was high in this cohort. Pregnancy planning programmes are needed for this population to decrease fetal and maternal complications and reduce vertical and horizontal transmission.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 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".