Decision-making about antidepressant medication use in pregnancy: a comparison between women making the decision in the preconception period versus in pregnancy
Bibliographic record
Abstract
BACKGROUND: Decisions about antidepressant use in pregnancy are complex. Little is known about how pregnancy-planning and already pregnant women making these decisions differ. METHODS: In 95 Canadian women having difficulty deciding whether to take antidepressants in pregnancy, we compared sociodemographic factors, clinical characteristics, and treatment intent between women planning pregnancy (preconception women) and currently-pregnant women. RESULTS: About 90% of preconception women (n = 55) were married or cohabitating and university-educated, and over 60% had an annual income of > 80,000 CAD/year; this was not different from currently-pregnant women (n = 40). Almost all women had previously used antidepressants, but preconception women were more likely to report current use (85.5% vs. 45.0%). They were more likely to have high decisional conflict (83.6% vs. 60.0%) and less likely to be under the care of a psychiatrist (29.1% vs. 52.5%). Preconception women were more likely than pregnant women to report the intent to use antidepressants (60% vs. 32.5%, odds ratio 3.11, 95% confidence interval 1.33-7.32); this was partially explained by between-group differences in current antidepressant use. CONCLUSIONS: Preconception women were more likely than pregnant women to intend to use antidepressants in pregnancy, in part because more of them were already using this treatment. Strategies to enhance support for decision-making about antidepressant medication use in pregnancy may need to be tailored differently for pregnancy-planning and already pregnant women.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".