Use of Mixed Methods Research in Research on Coronary Artery Disease, Diabetes Mellitus, and Hypertension
Bibliographic record
Abstract
BACKGROUND: Mixed methods research, the use of both qualitative and quantitative methods within 1 program of study, is becoming increasingly popular to allow investigators to explore patient experiences (qualitative) and also measure outcomes (quantitative). Coronary artery disease and its risk factors are some of the most studied conditions; however, the extent to which mixed methods studies are being conducted in these content areas is unknown. We sought to comprehensively describe the characteristics of published mixed methods studies on coronary artery disease and major risk factors (diabetes mellitus and hypertension). METHODS AND RESULTS: We conducted a scoping review of the literature indexed in PubMed, Medline, EMBASE, and CINAHL. We identified 811 abstracts for screening, of which 254 articles underwent full-text review and 97 reports of 81 studies met criteria for inclusion. The majority of studies in this area were conducted in the past 10 years by nurse researchers from the United States and United Kingdom. Diabetes mellitus was the most common content area for mixed methods investigation (compared with coronary artery disease and hypertension). Most authors described their rationale for using mixed methods as complementarity and did not describe study priority or how they reconciled differences in methodological paradigms. Some mixed methods study designs were more commonly used than others, including concurrent timing and integration at the interpretation stage. Qualitative strands were most commonly descriptive studies using interviews for data collection. Quantitative strands were most commonly cross-sectional observational studies, which relied heavily on self-report data such as surveys and scales. CONCLUSIONS: Although mixed methods research is becoming increasingly popular in the area of coronary artery disease and its risk factors, many of the more advanced mixed methods, qualitative, and quantitative techniques have not been commonly used in these areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.051 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".