The case for data sharing in traditional, complementary, and integrative medicine research
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
Traditional, complementary, and integrative medicine (TCIM) research encompasses a diverse range of health practices rooted in various cultural, philosophical, and historical frameworks. As global interest in conducting research in this field grows, the need for rigorous research to support the integration of evidence-based TCIM therapies into mainstream healthcare has become essential. Data sharing is critical to advancing TCIM research by enhancing reproducibility, fostering interdisciplinary collaboration, promoting ethical practices, and addressing global health challenges. Despite its benefits, numerous challenges are associated with data sharing in TCIM, including a lack of standardized practices, cultural sensitivity, intellectual property concerns, and technical barriers in resource-limited settings. This editorial explores the unique nature of TCIM research, emphasizing the importance of data sharing while acknowledging the complexities it entails. Implementing the CARE Principles for Indigenous Data Governance, which prioritize collective benefit, authority to control, responsibility, and ethics, offers a framework for ensuring that data sharing respects indigenous knowledge and cultural sensitivities. Strategies for overcoming barriers to data sharing include developing standardized protocols, investing in research infrastructure, and fostering a culture of openness and collaboration within the TCIM community and beyond. By advancing data sharing practices, TCIM research can contribute to evidence-based healthcare solutions and address global health disparities, ultimately improving health outcomes worldwide.
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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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