Placing Mixed Methods Research Within Hierarchies of Evidence in Health Sciences
Bibliographic record
Abstract
Policymakers, educationists, and social and health scientists use qualitative and quantitative hierarchies to evaluate evidence to guide practice and policymaking. Currently, a growing part of evidence is produced using mixed methods research (MMR) because this approach to inquiry is useful for exploring and understanding phenomena in different contexts and populations. However, an evidence hierarchy that refers to a system of ranking research designs as superior, inferior, or equal with respect to generating valid results for MMR is missing from the literature. The purpose of this article, therefore, is to identify eight essential and commonly addressed areas of MMR questions and propose a straightforward evidence hierarchy for mixed methods research in health sciences. This article also brings attention to the use and value of mixed methods for generating the highest quality evidence and extends the discussion regarding the value of mixed methods to guide practice and policymaking across various fields.
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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 | 0.545 | 0.562 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.036 | 0.019 |
| Science and technology studies | 0.013 | 0.065 |
| Scholarly communication | 0.041 | 0.037 |
| Open science | 0.007 | 0.030 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".