Pharmacology Specialty and Brain Drain are Factors for Consideration in Improving Pharmacology Education in Nigeria ‐ Lagos State University College of Medicine (LASUCOM) as Example
Bibliographic record
Abstract
INTRODUCTION In Nigeria, pharmacology departments teach medical students didactically towards direct health care delivery. Is there a premise for expanding pharmacology departments' programs to include science degree curricula using LASUCOM as example? METHODS Medical students of LASUCOM participated in different 18‐question surveys two months apart. RESULTS Respondents associated pharmacology with medicine (61.3%), science (24.9%), industry (16.8 %), and government (11.1%); 32.8% want to know clinical pharmacology, 7.1% basic pharmacology; 45.8% prefer to study lecturers' notes, 26.7% textbooks, 9.8% the Internet, and 2.7% journals;primarily to be able to treat patients (40%), obtain MBBS degree (39.1%) and 8.9% to know this subject, 3.1% to make money; 0.4% would definitely and 33.8% would probably become pharmacologists, while 27.1% are uninterested and 8.4% would never be pharmacologists. Regarding Nigeria as not yet developed (51%), most would visit a developed country (61%) after MBBS (52%); some to spend professional life (59.52%); highest interests are in USA (46.7%), Canada (44.8%) and any European country (40.48%); based on scientific and technical advancement (60%). Some 23.8% consider it important for self fulfillment; 32.9%, 26.2%, and 21.9% for opportunity, bright future, and job satisfaction; 63% would necessarily return to Nigeria for roots (39%), to bring expertise (51.9%) and experience (53.3%). CONCLUSION Respondents relate pharmacology to MBBS requirements; pharmacology departments could include science degree programs, collaborating with advanced countries, to contribute to local scientific and technical advancement, lack of which promotes brain drain.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".