A scoping review of decision aids for pregnant women around childbirth: Do they improve decision quality, processes, and outcomes?
Bibliographic record
Abstract
BACKGROUND: Meaningful engagement of women in childbirth decisions is a growing priority in maternity care. Decision aids are tools designed to improve decision quality, decision-making processes quality, care behaviours, and health outcomes (quadruple aim). OBJECTIVE: To map quantitative studies evaluating the effects of decision aids for women making decisions around childbirth. METHODS: We searched MEDLINE, Cochrane DSR and CENTRAL, CINAHL, and EMBASE (January 1975-April 2025) for reviews, randomized controlled trials and controlled before-and-after studies that assessed the effect of decision aids for childbirth decisions. RESULTS: 28 studies (11 reviews, 17 primary studies) met selection criteria. Most involved high-risk pregnant women and showed that decision aid improved the quality of decision and decision-making process. Positive impacts on care behaviour and health outcomes were more evident when decision aids were combined with other components and required discussions with healthcare providers. DISCUSSION: Although limited, evidence suggests decision aids are more effective when women are prepared to use the tool and healthcare providers are actively involved. The review identified gaps in research targeting low-risk women in low- and middle-income countries and assessing women's value and preferences for shared decision-making. CONCLUSIONS: Decision aids around childbirth have the potential to meet women's needs in decision-making, appropriate use of care and perinatal health outcomes especially when they are prepared to use the tool and supported by healthcare providers. Future research should evaluate all four aim objectives to strengthen the evidence on the benefits of decision aids, especially for low-risk population and in low- and middle-income countries.
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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 | 0.035 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".