Women's Knowledge of Future Cardiovascular Risk Associated With Complications of Pregnancy: A Systematic Review
Bibliographic record
Abstract
Background: Several common pregnancy conditions significantly increase a woman's risk of future cardiovascular diseases (CVD). Patient education and interventions aimed at awareness and self-management of cardiovascular risk factors may help modify future cardiovascular risk. The aim of this systematic review was to examine education interventions for cardiovascular risk after pregnancy, clinical measures/scales, and knowledge outcomes in published qualitative and quantitative studies. Methods: Five databases were searched (from inception to June 2023). Studies including interventions and validated and nonvalidated measures of awareness/knowledge of future cardiovascular risk among women after complications of pregnancy were considered. Quality was rated using the Mixed Methods Appraisal Tool. Results were analyzed using the Synthesis Without Meta-analysis reporting guideline. Characteristics of interventions were reported using the Template for Intervention Description and Replication. Fifteen studies were included; 3 were randomized controlled trials. Results: In total, 1623 women had a recent or past diagnosis of hypertensive disorders of pregnancy, gestational diabetes mellitus, and/or premature birth. Of the 7 studies that used online surveys or questionnaires, 2 reported assessing psychometric properties of tools. Four studies used diverse educational interventions (pamphlets, information sheets, in-person group sessions, and an online platform with health coaching). Overall, women had a low level of knowledge about their future CVD risk. Interventions were effective in increasing this knowledge. Conclusions: In conclusion, women have a low level of knowledge of risk of CVD after pregnancy complications. To increase this level of knowledge and self-management, this population has a strong need for psychometrically validated tailored education interventions.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".