Applying an Ethical Framework to Herbal Medicine
Bibliographic record
Abstract
Herbal medicines make a vital contribution to healthcare globally, but from production through to practice, there are ethical challenges that require attention. Ethical challenges are often analysed through application of an ethical framework because this can facilitate a consistent and structured approach. In healthcare, the most commonly used framework over recent decades has been that of the four principles: beneficence, nonmaleficence, autonomy, and justice. However, for various reasons that are explained, this approach to ethical analysis is not the most fitting for the global phenomenon of herbal medicine. In this paper, a relatively new moral framework that is based upon the globally accepted values of care, respect, honesty, and fairness is explored in relation to herbal medicine for the first time. Through application of this framework, the ethical challenges and actions needed to address them become clear, thus resulting in practical recommendations for enhancing ethical standards in herbal medicine.
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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 | 0.020 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".