Implementation of Total Quality Management in Higher Pharmaceutical Education: Opportunity and Challenge.
Bibliographic record
Abstract
Introduction: Higher pharmaceutical education in China has made a great development in recent years.A few problems, such as unevenness of school-running level, lack of students innovation and deficiency of teaching effect, are also exposed in its rapid development.The reasons for these challenges are also revealed that old teaching mode and increasing enrollment scale in China's university may be the primary causes.Methods: To face these problems, some measures should be taken.Total quality management (TQM), as a novel teaching concept, is proposed in this article to integrate into higher pharmaceutical education in China.Results: To implement TQM, suggestions are given to emphasize on professional ethics, course quality and practice teaching.Firstly, the educators should take phar macy moral as one of the important contents and infiltrate it in all teaching activities so as to realize the cultivation of professional ethics.In addition, in order to improve course quality, it is necessary to implement curriculum reform by broadening curriculum caliber, optimizing curriculum system and removing regional segmentation in professional courses.What's more, practice teaching should be actively adopted to combine itself with higher pharmaceutical education in China due to it being a discipline depending largely on the practice.Conclusion: It is necessary to boost the TQM step by step within a healthy system, which will play an important role in improving the quality of pharmaceutical education.Though implementation of TQM in higher pharmaceutical education faces various opportunity and challenge, the philosophy of TQM will be gradually accepted by more and more universities.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".