A World of Possibilities in Mixed Methods: Review of the Combinations of Strategies Used to Integrate Qualitative and Quantitative Phases, Results and Data
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
Mixed methods (MM) are increasingly popular.Researchers integrate qualitative (QUAL) and quantitative (QUAN) methods (e.g., research questions, data collections and analyses, and results).Several integration strategies have been proposed, but their conceptualization is usually design-driven, or fragmented, or not empirically tested.This is challenging for planning and conducting MM studies, and for training graduate students.Based on the methodological literature, we developed a conceptual framework including types of integration and practical strategies, and possible combinations.Then, we tested this framework using 93 health-related 2015 MM studies with a method-detailed description, which illustrated all types of combinations.Our work contributes to advance methodological knowledge on MM via (a) a call for better reporting health-related MM studies, and (b) a tested conceptualisation comprising 3 types of integration and 9 specific strategies, which explain current and future possibilities for combining strategies to integrate QUAL and QUAN phases, results, and data.
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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 | 0.034 | 0.056 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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