A sequential explanatory mixed methods study design: An example of how to integrate data in a midwifery research project
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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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 | 0.353 | 0.251 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier 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".