Women's Knowledge of Future Cardiovascular Risk Associated With Complications of Pregnancy: A Systematic Review
Why this work is in the frame
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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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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.016 | 0.004 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it