Evaluation of a Postpartum Cardiovascular Prevention Clinic After Hypertensive Disorders of Pregnancy: A Mixed-Methods Study
Bibliographic record
Abstract
OBJECTIVES: After experiencing a hypertensive disorder of pregnancy, women are at increased risk of developing cardiovascular (CV) risk factors and premature CV disease. A dedicated postpartum clinic is 1 potential solution to educate patients on how to reduce their long-term health risks but there are limited data from the patient perspective regarding the utility and quality of such interventions. METHODS: We performed a mixed-methods study using questionnaire data and focus group interviews to assess the patient's perspective regarding the timing, content, modality (virtual vs. in-person) and perceived effectiveness of the Postpartum CV Prevention Clinic in Ontario, Canada. RESULTS: Participants reported improved understanding of their health condition and healthy behaviours, as well as concrete behavioural changes because of their experience in the clinic. Participants reported a need for more mental health resources as part of their postpartum follow-up. A combination of in-person and virtual care was the preferred modality of follow-up. CONCLUSIONS: A dedicated Postpartum CV Prevention Clinic is an effective means to educate patients on health behaviours and the need for follow-up after pregnancy, but gaps remain in care, and future research is needed to determine the long-term health impacts.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".