A sequential explanatory mixed methods study design: An example of how to integrate data in a midwifery research project
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Integration of mixed methods involves bringing together quantitative and qualitative approaches and methodologies. Limited application in midwifery research has identified a need for practical examples. How to integrate two research approaches and methodologies in a sequential explanatory mixed methods study, at the design, methods, interpretation and reporting levels will be explained. This paper describes and discusses an example of how integration was used to develop a better understanding of midwives’ knowledge and confidence after attending a healthy eating education workshop/webinar. This example illustrates how integration can be achieved and emphasises how a weaving technique can be used, and findings are presented in a joint display and extreme case analysis. The sequential explanatory design was adopted to merge and mix different datasets to be collected and analysed. Then, using meta-analysis to identify areas of convergence or discordance, which provided a more comprehensive overview and understanding of the key themes that linked midwives' knowledge and confidence. The application of this mixed methods design assisted in investigating and exploring midwives' knowledge and confidence levels and provided clear insights for midwives needs and the effectiveness of healthy eating education on practice.
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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 | 0.043 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it