Framework and key considerations for designing and conducting critical mixed-methods research (cMMR)
Bibliographic record
Abstract
Mixed-methods research leverages the strengths of qualitative and quantitative methodologies and methods to generate a comprehensive understanding of phenomena. There is ample guidance about traditional mixed-methods research, but limited discussion on critical mixed-methods research. To provide a conceptual framework and practical considerations for designing and conducting critical mixed-methods research. Contemporary and classical literature in critical research and mixed methods was used to guide the development of this framework. Critical mixed-methods research is informed by critical social theory, critical dialectical pluralism and transformative worldview. The seven core features of critical mixed-methods research are critique, insight, transformation, engagement, epistemic inclusion, critical reflexivity and intersectionality. Critical mixed methods is necessary and needed for studying social justice and equity-related research problems in health sciences and public health. Using the proposed framework and practical strategies can enable researchers to unravel complex social and healthcare phenomena through addressing power, oppression and social justice issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.483 | 0.335 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.021 | 0.010 |
| Open science | 0.011 | 0.014 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".