How can meta-research be used to evaluate and improve the quality of research in the field of traditional, complementary, and integrative medicine?
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.
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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 | 0.725 | 0.875 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.036 | 0.025 |
| Bibliometrics | 0.037 | 0.029 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.047 | 0.057 |
| Open science | 0.016 | 0.019 |
| Research integrity | 0.022 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".