Using mixed methods research to study research integrity: Current status, issues, and guidelines
Bibliographic record
Abstract
Background: The multifaceted nature of research integrity (RI) calls for the adoption of innovative methodologies to achieve a more thorough understanding. Mixed methods research (MMR) provides a valuable framework by combining diverse data sources, enabling a more nuanced exploration of complex research questions.Methods: This paper reviews seven RI studies employing MMR to identify methodological shortcomings. It introduces key concepts and typologies of MMR and proposes actionable strategies to enhance methodological rigor and innovation.Results: The review identified three key issues in current MMR applications: 1. Insufficient articulation of methodological contributions. 2. Limited visualization of quantitative and qualitative data integration. 3. Minimal engagement with recent MMR advancements. To address these gaps, a targeted To-Do List was created, offering actionable strategies for improving methodological rigor. Additionally, underutilized MMR designs, such as convergent and exploratory sequential designs, were recommended to strengthen data synthesis and expand analytical perspectives.Conclusions: MMR provides valuable opportunities to enhance RI research. This paper offers practical guidance for adopting MMR, addressing methodological gaps, and fostering robust, integrative research practices.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.706 | 0.799 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.023 | 0.028 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.034 | 0.029 |
| Open science | 0.012 | 0.018 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".