Interdisciplinary mixed methods systematic reviews: Reflections on methodological best practices, theoretical considerations, and practical implications across disciplines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.019 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 it