Study Destination Choice and Career Preferences of International Students at China Pharmaceutical University
Bibliographic record
Abstract
This study had two main thematic aims: first, to determine the factors that influenced the choice of international students at China Pharmaceutical University (CPU) to consider China as a study destination; second, to determine the career preferences of international students upon completion of their various programs of study. As a cross-sectional study, relevant data were collected from undergraduate pharmacy students and postgraduates using a self-administered questionnaire. On the whole, the five most important pull factors that influenced the choice of China as the study destination for the respondents were: (1) quality of education, (2) quality of academic staff, (3) security, (4) desire to obtain a certificate from a foreign country and (5) availability of scholarship opportunities. With respect to the career choices, the top three career preferences of the international students were: (1) to work in the pharmaceutical industry (i.e., pharmaceutical manufacturing companies), (2) to practice clinical pharmacy and (3) to seek employment opportunities outside their countries. This study provides additional details on why China has gradually become a study destination of choice for international students. The career preferences of students could be useful in the design of academic programs that could meet their job aspirations.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".