Making the shift from unknowing to knowing and living with one's risk for coronary artery disease after having had gestational diabetes mellitus: a grounded theory study
Bibliographic record
Abstract
Background: Coronary artery disease (CAD) is a growing cardiovascular issue for women under the age of 55, resulting in poor health outcomes, including mortality. The literature has identified that women possess both traditional and pregnancy-related nontraditional risk factors for CAD. Gestational diabetes mellitus (GDM) is one such risk factor that is on the rise, causing a fourfold increased risk for CAD. Although the connection between the risk of CAD following GDM exists, it remains unclear if, and from whom, when, and how women acquire their knowledge of this risk. It is also uncertain how they come to understand and manage this risk. Furthermore, specific follow-ups for the development of CAD after GDM are not being conducted. We need a clearer understanding of how these women come to know, understand, assign meaning to, manage, and live with this risk before education, screening, and interventions can be developed. Purpose: The purpose of this grounded theory (GT) study was to gain a fuller understanding of the psychosocial process experienced by women who had GDM as they assign meaning to the risk for CAD and make coinciding decisions about their future health and well-being. Methods: This research study was guided by a GT approach. Semi-structured interviews were conducted on women who had GDM and lived in Newfoundland and Labrador (NL). The constant comparative method was used to facilitate data collection and analysis. Results: There were 26 women from NL with a history of GDM who participated in the study. The substantive theory that emerged from the data was Making the Shift from Unknowing to Knowing and Living with One's Risk for CAD After Having had GDM. This substantive theory emerged from three theoretical constructs: 1) Sustaining a Sense of Unknowing About the Risk for CAD Following a Diagnosis of GDM, 2) Knowing One's Risk for CAD Following a Diagnosis of GDM, and 3) Living With One's Risk for CAD Following a Diagnosis of GDM. The findings of the first theoretical construct indicate that women were not informed about their risk for CAD after GDM, during their GDM diagnosis, or after their pregnancy. The findings of the second theoretical construct reveal that the participants did not know they were at risk for CAD after receiving a diagnosis of GDM. The third theoretical construct identifies the barriers and motivators associated with implementing lifestyle and behavioural changes in individuals living with the risk of CAD following a GDM diagnosis. It highlights the need for clinical practice guidelines and follow-up for this cohort. Conclusions: Women living in NL require better education about CAD risk from healthcare professionals (HCPs), who in turn need training to communicate this information effectively. Therefore, improved risk communication by HCPs is crucial. Specific clinical guidelines and screenings should be created for this group to mitigate CAD risk. Additionally, a dedicated women's health center should adopt a sex-and-gender focus with an interdisciplinary team for those with GDM and other risk factors for CAD. Nurses can serve as navigators and educators in this team, ensuring that women's healthcare experiences are recognized.
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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 | 0.031 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".