Neuroscience education and research in Cameroon: Current status and future direction
Bibliographic record
Abstract
Neurological disorders comprise 20% of hospital admissions in Cameroon. The burden of neurological disorders is increasing, especially in children and the elderly. However, there are very few neurologists, psychiatrists, gerontologists and neuropsychologists trained in the treatment of neurological disorders in Cameroon and there are very few facilities for training in basic and clinical neuroscience. Although non-governmental organizations such as the International Brain Research Organization (IBRO), International Society of Neurochemistry (ISN), and Teaching and Research in Natural Sciences for Development (TReND) in Africa have stepped in to provide short training courses and workshops in neuroscience, these are neither sufficient to train African neuroscientists nor to build the capacity to train neuroscience researchers and clinicians. There has also been little support from universities and the government for such training. While some participants of these schools have managed to form collaborations with foreign researchers and have been invited to study abroad, this does not facilitate the training of neuroscientists in Cameroon. Moreover, the research infrastructure for training in neuroscience remains limited. This is reflected in the low research output from Cameroonian universities in the field. In this review, we describe the burden of neurological disorders in Cameroon and outline the outstanding efforts of local scientists to develop the discipline of neuroscience, which is still an emerging field in Cameroon. We identify key actionable steps towards the improvement of the scientific capacity in neuroscience in Cameroon: (1) develop targeted neuroscience training programs in all major universities in Cameroon; (2) implement a thriving scientific environment supported by international collaborations; (3) focus on the leadership and the mentorship of both local and senior neuroscientists; (4) develop public awareness and information of policy makers to increase governmental funding for neuroscience research.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".