Previous experiences of pregnancy and early motherhood among women living with HIV: a latent class analysis
Bibliographic record
Abstract
Previous maternity experiences may influence subsequent reproductive intentions and motherhood experiences. We used latent class analysis to identify patterns of early motherhood experience reported for the most recent live birth of 905 women living with HIV enrolled in the Canadian HIV Women's Sexual and Reproductive Health Cohort Study (CHIWOS). Four indicators were used: difficulties getting pregnant, feelings when finding out pregnancy, feelings during pregnancy, and feelings during the first year postpartum. Most (70.8%) pregnancies analyzed occurred before HIV diagnosis. A four-class maternity experience model was selected: "overall positive experience" (40%); "positive experience with postpartum challenges" (23%); "overall mixed experience" (14%); and "overall negative experience" (23%). Women represented in the "overall negative experience" class were more likely to be younger at delivery, to not know the HIV status of their pregnancy partner, and to report previous pregnancy termination. Women represented in the "positive experience with postpartum challenges" class were more likely to report previous miscarriage, stillbirth or ectopic pregnancy. We found no associations between timing of HIV diagnosis (before, during or after pregnancy) and experience patterns. Recognition of the different patterns of experiences can help providers offer a more adapted approach to reproductive counseling of women with HIV.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".