How can meta-research be used to evaluate and improve the quality of research in the field of traditional, complementary, and integrative medicine?
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
The field of traditional, complementary, and integrative medicine (TCIM) has garnered increasing attention due to its holistic approach to health and well-being. While the quantity of published research about TCIM has increased exponentially, critics have argued that the field faces challenges related to methodological rigour, reproducibility, and overall quality. This article proposes meta-research as one approach to evaluating and improving the quality of TCIM research. Meta-research, also known as research about research, can be defined as "the study of research itself: its methods, reporting, reproducibility, evaluation, and incentives". By systematically evaluating methodological rigour, identifying biases, and promoting transparency, meta-research can enhance the reliability and credibility of TCIM research. Specific topics of interest that are discussed in this article include the following: 1) study design and research methodology, 2) reporting of research, 3) research ethics, integrity, and misconduct, 4) replicability and reproducibility, 5) peer review and journal editorial practices, 6) research funding: grants and awards, and 7) hiring, promotion, and tenure. For each topic, we provide case examples to illustrate meta-research applications in TCIM. We argue that meta-research initiatives can contribute to maintaining public trust, safeguarding research integrity, and advancing evidence based TCIM practice, while challenges include navigating methodological complexities, biases, and disparities in funding and academic recognition. Future directions involve tailored research methodologies, interdisciplinary collaboration, policy implications, and capacity building in meta-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 | 0.046 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.009 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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