Using legitimation criteria to establish rigour in sequential mixed-methods research
Bibliographic record
Abstract
BACKGROUND: Despite the extensive use of mixed methods across health sciences, there has been a limited discussion about the methodological rigour and quality in mixed methods research (MMR). Although the empirical and methodological literature about mixed methods is increasing, there are few practical examples of the implementation of rigour criteria. AIM: To discuss and illustrate the application of 'legitimation criteria' to the design and conduct of a sequential exploratory MMR study of nurse educators' challenges when teaching undergraduate students. DISCUSSION: The legitimation criteria can establish philosophical and methodological validity and rigour in MMR. MMR is complex and daunting, so maintaining rigour is crucial in ensuring the conclusions drawn are plausible and researchers, practitioners and policymakers use them to guide research and practice. CONCLUSION: The legitimation criteria are specific to MMR and are useful in improving the conduct and execution of studies. They enable researchers to maintain quality throughout their studies, from the development of a research question to the generation of conclusions. IMPLICATIONS FOR PRACTICE: This illustration of the legitimation criteria for the design and conduct of MMR will guide researchers in establishing rigour and lessen the threats to their studies' validity.
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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 | 0.829 | 0.929 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.013 | 0.041 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".