What Women Said: Qualitative Study of Pre-Eclampsia Beliefs

The PLOS One study found that some participants viewed themselves as unlikely to develop pre-eclampsia because they were young, healthy, symptom-free, or had no family history of pregnancy-related high blood pressure.

Other participants felt more vulnerable after seeing friends or relatives develop pre-eclampsia. One participant said, “I have never had pre-eclampsia myself, but I watched my close friend suddenly develop it during her second trimester last year.” The contrast shows why qualitative analysis must preserve differences between general awareness and personal susceptibility.

The PLOS One researchers found that antenatal monitoring, clear guidance from clinicians, previous experience with pre-eclampsia, and awareness of warning signs acted as cues to action.

Participants also described self-efficacy through home blood pressure monitoring, recording readings, reporting symptoms, and following advice about rest and diet. One participant said, “I write down all the advice from each clinic visit in a notebook…… I even learned to record my daily BP checks in a book and take it along with me to the clinic.”

The study identified financial costs, transport problems, childcare responsibilities, family control, mistrust of hospitals, negative experiences with staff, and unclear explanations as barriers to care-seeking.

The findings suggest that health education alone may not remove barriers when women cannot afford transport or medication, fear disrespectful treatment, or leave appointments without understanding what symptoms require urgent attention.

Maternal health researchers can translate these findings into action by separating perceived susceptibility, perceived severity, benefits, barriers, cues, and self-efficacy during coding and then comparing how those themes vary across participant groups.

Researchers can use interview evidence to distinguish communication problems from access problems. That distinction can guide different interventions, such as clearer risk explanations, respectful-care training, transport support, or community-based health information.

Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.

Evidano can support thematic, content, frequency, and cross-segment analyses across interview transcripts and research documents. A maternal health team could organize findings around the six Health Belief Model constructs, then compare barriers by participant characteristics or care experience.

Researchers can learn more about the platform’s relevant analysis capabilities on the Evidano features page.

Evidano’s AI chat over documents and analyses can help researchers locate supporting excerpts, ask focused questions about recurring beliefs, and review how themes are expressed across interviews.

Direct quotations should remain connected to the source transcript and should be checked by researchers before publication, especially in sensitive maternal health research.

Evidano supports transcription with PII redaction and encrypted data handling, which is relevant when research teams process pregnancy, diagnosis, or healthcare-access interviews.

Evidano states that customer data is never used to train third-party models. Research teams should still follow their approved consent, ethics, retention, and de-identification procedures.

The PLOS One study found that participants generally viewed pre-eclampsia as serious but differed in how susceptible they believed they were.

The study also found that antenatal care and professional guidance encouraged action, while financial constraints, mistrust, family influence, and inadequate communication created barriers.

The PLOS One study included 16 pregnant women interviewed at a district hospital in Ghana during October 2021.

The researchers assessed saturation after 14 interviews and conducted 2 more interviews to check whether substantially new findings emerged.

The researchers used qualitative descriptive interviews and qualitative content analysis, beginning with inductive coding and then applying the Health Belief Model as a deductive interpretive framework.

The PLOS One article reports that the team developed meaning units, condensed meaning units, codes, subcategories, and broader categories before relating the categories to the model’s six constructs.

The Health Belief Model is a framework that examines perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy.

The PLOS One researchers used the model to interpret how women’s beliefs about risk, benefits, barriers, and capability shaped engagement with maternal healthcare.

AI can help researchers organize and compare qualitative interviews about pre-eclampsia, but researchers must verify codes, quotations, interpretations, and ethical safeguards.

An AI-enabled platform such as Evidano can assist with thematic and cross-segment analysis while keeping human researchers responsible for methodological judgment and participant protection.

The main limitation is that the study involved 16 purposively selected women from one Ghanaian health facility, so the findings are context-specific rather than statistically representative.

The PLOS One authors also noted possible recall bias, socially desirable responses, and the potential influence of the researchers’ clinical backgrounds and familiarity with the Health Belief Model.