Toward a Framework for Appraising the Quality of Integration in Mixed Methods Research
Bibliographic record
Abstract
Although integration is a crucial element of mixed methods research (MMR), most MMR quality frameworks have not comprehensively addressed integration in their criteria. These frameworks tend to focus on whether integration is present, without considering important aspects such as the rigor of the integration processes used or their consistency with the other components of the MMR study. This paper presents the Mixed Methods Integration Quality Framework (MMIQF), which was developed based on a methodological review of the literature on integration in MMR. The proposed framework is intended to be useful to authors and readers of MMR studies who wish to ensure and appraise the appropriate implementation of integration in the design, conduct, and reporting of MMR studies.
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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 | 0.844 | 0.843 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.042 | 0.022 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.035 | 0.026 |
| Open science | 0.012 | 0.032 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".