Women's decision making about antidepressant use during pregnancy: A narrative review
Bibliographic record
Abstract
BACKGROUND: Depression is common, particularly among women of childbearing age, and can have far-reaching negative consequences if untreated. Efficacious treatments are available, but little is known about how women make depression treatment decisions during pregnancy. The purpose of this narrative review is to interpretively synthesize literature on women's decision making (DM) regarding antidepressant use during pregnancy. METHODS: The databases PubMed, CINAHL, and PsycINFO were searched between May 2015 and August 2017 for peer-reviewed, English-language papers using terms such as "depression," "pregnancy," and "DM." The literature matrix abstraction method was used to systematically abstract data from full articles that met criteria for inclusion. RESULTS: Of the articles abstracted (N = 10), half did not cite a DM theory on which the work was based. Key aspects of DM for this population were need for information and decision support, desire for active participation in DM, reflection on beliefs and values, evaluation of treatment option sequelae, and societal expectations. Treatment DM for depression during pregnancy is particularly impacted by the stigma associated with depression and societal expectations of pregnant women related to medication use during pregnancy. These findings, however, were based on studies of predominantly Caucasian and well-educated women. CONCLUSIONS: Women require a nonjudgmental environment, in which shared DM feels safe, to foster positive DM experiences and outcomes. Future research is needed to define how to best support women to make depression treatment decisions in pregnancy, with particular attention to DM in the second and third trimesters of pregnancy.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".