A review of scientific research on the implementation of traditional medicine in rehabilitation
Bibliographic record
Abstract
Traditional medicine (TM) is an expression used for a part of alternative medicine or medicine that is grounded on a folk knowledge. Regardless of the official position of the medicine, the use of methods of traditional medicine is constantly increasing, especially in the most developed countries. For example, 70% of the population in Canada and 80% in Germany are using some form of alternative or complementary medicine. In traditional medicine, the emphasis is on balance or 'harmony' in the body, since the body is associated with the earth and the cosmos which is very important for the healing process. There are contrary opinions that claim that TM is a huge collection of never proven theory. A new way of looking and interpreting of all the evidence and observations about the processes of sickness and health is necessary for proving TM. There are enormous differences between TM and conventional medicine, both in the methods used for any kind of intervention and in the way doctors and healers are trained. It is also important to note that studies in the field of TM are using norms and standards used for research in conventional medicine, which means that they are not adapted to the basic characteristics of TM. Therefore, scientific research on the importance of using TM is a continuous process. The presented research results demonstrate the positive effects of the use of TM methods in rehabilitation.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".