Workforce, regulation and capacity needed for integration of traditional medicine
Bibliographic record
Abstract
The widespread use of traditional, complementary and integrative medicines (traditional medicine) across the world suggests that integration of traditional medicine into the formal health system is one strategy for extending universal health coverage (UHC). To improve access to and the quality of traditional medicine services will require attention to strengthening the traditional medicine workforce. The challenges associated with making such improvements should not be underestimated due to the many different practices, service delivery models and education systems for traditional medicine, as well as relevant policy and governance frameworks. Countries have adopted varying strategies to integrate traditional medicine into health systems to date. We consider how to strengthen and build capacity of the traditional medicine workforce so it might better contribute to the UHC agenda. We examine key issues and challenges for traditional medicine, and suggest analytical models for understanding the complexity inherent to integration of traditional medicine and making sense of different components of the traditional medicine workforce.
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.
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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".