The Role of Emotion in Healthcare Decision Making in Pregnancy : Findings From a Qualitative investigation
Bibliographic record
Abstract
BackgroundThe leading cause of maternal morbidity and mortality worldwide, hypertensive disorders of pregnancy affect between 5% and 10% of pregnancies in Canada. (1,2) New clinical guidance for management of pregnancy hypertension recommends considering womenu2019s preferences before making treatment decisions. (3) While new work has investigated womenu2019s preferences and decisional needs for pregnancy hypertension, (4) research has yet to explore emotional responses to diagnosis and treatment, despite evidence that anxiety is heightened during pregnancy (5) and emotional responding is important for decision-making. (6) MethodsA qualitative approach was used. Working with two patient partners, we developed an interview approach and topic guides for focus groups and individual interviews. Semi-structured focus groups and individual interviews were conducted in an iterative fashion, with memoing and debriefing after each group meeting or interview. All interactions will be transcribed for constant comparison grounded in a critical realist perspective. Codes will be developed inductively and collected into themes that will reviewed with the patient partners, and then the research team for face validity. Results28 women participated in two focus groups and 20 individual interviews. Preliminary analysis identified three broad themes relating to emotional responses in the context of pregnancy hypertension: 1)tInformation seeking/avoidance2)tImportance of self-care3)tTaking cues about anxiety and stress from others4)tAnxiety caused by treatment requirementsDiscussionPreliminary analysis indicates that emotion plays a large role in how women experience and navigate healthcare decision-making in pregnancy hypertension. Next steps will be exploring if ability to manage emotions impacts aspects of healthcare decision-making in pregnancy (i.e., ability to understand and remember new information).Dissemination PlanResults from this work will be shared at conferences directed at patient and professional audiences and will be incorporated into the design of a publicly available patient decision aid.AcknowledgementsThis work was funded by the BC SUPPORT Unit and the Canadian Institutes of Health ResearchReferences1. Ghulmiyyah L, Sibai B. Maternal mortality from preeclampsia/eclampsia. Semin Perinatol. 2012 Feb;36(1):56u20139.2. Lo JO, Mission JF, Caughey AB. Hypertensive disease of pregnancy and maternal mortality: Curr Opin Obstet Gynecol. 2013 Apr;25(2):124u201332. 3. Butalia S, Audibert F, Cu00f4tu00e9 A-M, Firoz T, Logan AG, Magee LA, et al. Hypertension Canadau2019s 2018 Guidelines for the Management of Hypertension in Pregnancy. Can J Cardiol. 2018 May 1;34(5):526u2013314. Metcalfe RK, Harrison M, Hutfield A, Lewish M, Singer J, Magee LA, et al., Patient Preferences and Decisional Needs When Choosing a Treatment Approach for Pregnancy Hypertension: A Stated Preference Study (Under Review).5. Ross LE, McLean LM. Anxiety disorders during pregnancy and the postpartum period: A systematic review. The Journal of clinical psychiatry. 2006 Aug.6. Schwarz N. Emotion, cognition, and decision making. Cognition & Emotion. 2000 Jul 1;14(4):433-40.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| 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".