Interdisciplinary mixed methods systematic reviews: Reflections on methodological best practices, theoretical considerations, and practical implications across disciplines
Bibliographic record
Abstract
Literature reviews have recently gained increased recognition for their value in advancing knowledge and decision making across disciplines. While interdisciplinary mixed-methods systematic reviews create opportunities to synthesize knowledge and insights from across disciplines, it is important to highlight the challenges, successes, and theoretical and practical considerations in combining research from various disciplines and research methodologies. In this article, we reflect on our experiences with conducting an interdisciplinary mixed-methods systematic review and outline theoretical and practical considerations involved in ensuring that methodologically rigorous, transparent, and meaningful research was achieved. As a group of academics from Education, Medicine, Nursing, and Social Work, who worked together on an interdisciplinary mixed-methods systematic review, we offer insights from our personal experiences as a transparent exemplar for how we embraced the challenges in conducting our project and managed the bottlenecks that often occur in interdisciplinary research.
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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 | 0.796 | 0.751 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.018 | 0.056 |
| Scholarly communication | 0.052 | 0.064 |
| Open science | 0.015 | 0.043 |
| Research integrity | 0.029 | 0.054 |
| 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".