Approaches to Education of Pharmaceutical Biotechnology in Faculties of Pharmacy
Bibliographic record
Abstract
Pharmaceutical biotechnology is developing rapidly both in academic institutions and in the biopharmaceutical industry. For this reason, FIP Special Interest Group of Pharmaceutical Biotechnology decided to develop a questionnaire concerning pharmaceutical biotechnology education. After preliminary studies were completed, questionnaires were sent to the leading scientists in academia and research directors or senior managers of various Pharmaceutical Biotechnology Companies in order to gather their views about how to create a satisfactory program. The objectives of this study were as follows: -To review all of the graduate and undergraduate courses which are presently available worldwide on pharmaceutical biotechnology in Faculties of Pharmacy. -To review all of the text books, references and scientific sources available worldwide in the area of pharmaceutical biotechnology. When replying to the questionnaires, the respondents were asked to consider the present status of pharmaceutical biotechnology education in academia and future learning needs in collaboration with the biotechnology industry. The data from various pharmacy faculties and biotechnology industry representatives from Asia, Europe and America were evaluated and the outcome of the survey showed that educational efforts in training qualified staff in the rapidly growing field of pharmaceutical biotechnology is promising. Part of the results of this questionnaire study have already been presented at the 57th International Congress of FIP Vancouver, Canada in 1997.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".