Patient Perspectives on a Pilot Virtual Follow-up Program After Hypertensive Disorders of Pregnancy: A Qualitative Study
Bibliographic record
Abstract
Background: Hypertensive disorders of pregnancy (HDPs) are a risk factor for future cardiovascular disease; therefore, follow-up and implementation of early interventions is recommended. We performed a qualitative study to assess the feasibility and user response to a mobile-health tool and virtual consultation aimed at educating people with an HDP on future cardiovascular risk, and at better understanding patients' priorities for postpartum care. Methods: Participants with a history of an HDP in the past 5 years had access to an online educational tool and participated in a virtual consultation to discuss their cardiovascular risks after experiencing an HDP. Participants were invited to a focus group to obtain feedback on their postpartum experience and the Her-HEART program. Results: A total of 20 female participants were enrolled in the study between January 2020 and February 2021. Of these, 16 participants took part in 1 of 5 focus groups. Participants reported a lack of awareness of future cardiovascular disease risks prior to participating in the program, and identified barriers to counselling, including traumatic birth experiences, inappropriate timing, and competing priorities. Participants reported that the virtual Her-HEART program was an effective avenue to provide counselling on long-term cardiovascular risks. They highlighted the importance of coordinated care pathways and mental health support in postpartum follow-up programs. Conclusion: We have shown the feasibility of providing an educational website and virtual consultation to facilitate counselling in people affected by HDPs. Our results shed light on patient-reported priorities related to the content and delivery of postpartum counselling after an HDP.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".