Ghana’s preparedness to exploit the medicinal value of industrial hemp
Bibliographic record
Abstract
Abstract Background Interest in industrial hemp is increasing steadily, as can be seen by the growing number of countries that have either decriminalized industrial hemp or are contemplating its decriminalization. In line with this trend, Ghana recently decriminalized the cultivation of industrial hemp (the cannabis variety with low Δ9-tetrahydrocannabinol (THC) and high cannabidiol (CBD) content), resulting in the need for research into its benefits to Ghanaians. This article examines cannabis (including industrial hemp) production, facilities for industrial hemp exploitation, and the potential benefits of industrial hemp in Ghana. Main body Indigenous cannabis strains in Ghana have high THC to CBD ratios suggesting the need for the government to purchase foreign hemp seeds, considering that the alternative will require significant research into decreasing the THC to CBD ratio of indigenous cannabis strains. Furthermore, there are several facilities within the country that could be leveraged for the production of medicinal hemp-based drugs, as well as the existence of a number of possible regulatory bodies in the country, suggesting the need for less capital. Research has also shown the potential for treatment of some medical conditions prevalent among Ghanaians using medicinal hemp-based products. These reasons suggest that the most feasible option may be for the government to invest in medicinal hemp. Conclusion Considering the challenges associated with the development of other hemp-based products, the availability of resources in the country for exploitation of medicinal hemp, and the potential benefits of hemp-based drugs to Ghanaians, investing in medicinal hemp may be the best option for the government of Ghana.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".