Bibliometric analysis of beneficial cannabis research: Performance analysis and science mapping from 2012 to 2022 and focus on Morocco
Bibliographic record
Abstract
Beneficial cannabis use has sparked growing interest among researchers, leading to an increase in empirical studies exploring its phytochemistry and applications. However, understanding the overall research orientation remains limited. This study aims to bridge this gap by conducting a bibliometric analysis of 7841 documents published from 2012 to 2022. The analysis reveals an annual growth rate of 16.83 %, with a focus on medicine, pharmacology, toxicology, pharmaceutics, biochemistry, genetics, molecular biology, and neuroscience. Performance analysis highlights metrics of sources, countries, affiliations, and authors, while science mapping identifies keywords, thematic evolution, and citation/co-authorship patterns. Notably, Morocco, despite its limited initial contributions, has shown recent steady growth in cannabis research, with an annual growth rate of 14.31 % and a 51.72 % international collaborative rate. This study provides valuable insights into established fields and potential research directions in cannabis research, paving the way for a deeper understanding among the audience. With the changing legal status of cannabis, research is rapidly expanding, focusing on the plant's bioactive compounds, pharmacological properties, and therapeutic applications. The dominant subject areas are medicine, pharmacology, toxicology, pharmaceutics, biochemistry, genetics, molecular biology, and neuroscience, covering nearly 76 % of the studied papers. Despite limited initial contributions from African countries like Morocco due to legal restrictions, beneficial cannabis research is gaining interest. Future research should prioritize in-depth exploration of specific compounds, comparative studies of cannabis-based products, and rigorous clinical trials. Fostering international collaborations and bridging the gap between research and policymakers are crucial for harnessing the full potential of cannabis while mitigating potential risks. This study serves as a reference for researchers to identify current orientations, blind areas, and gaps in cannabis research, offering suggestions for future studies. • Interest in beneficial cannabis research is translated by a growth factor reaching 373.73 % from 2012 to 2022. • The studied papers scored an h-index of 172, and more than 176 000 citations. • The United States, Canada, the United Kingdom, Italy are the biggest academically influential contributors to the field. • The studies documents cover mainly these subject areas: Medicine, Pharmacology, toxicology, and pharmaceutics, Biochemistry, Genetics and molecular biology, and Neuroscience. • Morocco is marking his way in beneficial cannabis research through a growth factor of 233 % (2013–2022), and focusing on international collaborations, with a rate reaching 51.72 %.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.055 | 0.091 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".