Protocol for a Scoping Review of Traditional and Complementary Medicine Governance Across Sub-Saharan Africa
Bibliographic record
Abstract
Background: Since 1978, the World Health Organization (WHO) has repeatedly called on Member States to recognize the role of traditional and complementary medicine (T&CM) in primary healthcare, improve safety, and accessibility by governing T&CM. In the 2019 Global Report on T&CM, the WHO reported that 40 out of 47 (85%) Member States from African Region had enacted governance policies, and 20 out of 47 (43%) had regulatory policies on herbal medicines. The primary barriers to implementing T&CM policy were identified as an absence of data and inadequate financial support for research. The objective of this protocol was to detail how to perform a scoping review that will examine the policy, legislative, and regulatory landscape for T&CM practitioners and products in sub-Saharan Africa.Methods: Databases will be searched (AMED, CINAHL Plus with Full Text, MEDLINE Plus with Full text, Web of Science, Scopus, PubMed, Google Scholar) for relevant articles. Searches will be limited to English, French, Portuguese, and Spanish language studies in peer-reviewed journals (1963-2023) that substantively report on legislation, bills, policies, governance approaches and regulations on T&CM (including successes and/or challenges in their design and implementation). Actual legislation, policies, and regulatory documents on T&CM and peer-reviewed studies with emphasis on integrating T&CM and biomedicine into healthcare systems will be excluded.Expected Outcomes: This protocol has formulated the objectives for a scoping review to identify, map, and synthesize evidence on the governance of T&CM in sub-Saharan Africa.
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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 | 0.142 | 0.171 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.026 | 0.020 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.016 | 0.011 |
| Insufficient payload (model declined to judge) | 0.141 | 0.027 |
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".