Academic Career Progression of Chinese-Origin Pharmacy Faculty Members in Western Countries
Bibliographic record
Abstract
Background: The field of Pharmacy education is experiencing a paucity of underrepresented minorities (URMs) faculty worldwide. The aim of this study is to investigate the current professional status of Chinese-origin pharmacy faculty members, who are considered as a good model of URMs at pharmacy academia in western countries, and identify the influencing factors to their academic career progression in academic careers. Methods: An online questionnaire was sent to Chinese-origin academic staffs at pharmacy schools in US, UK, Canada, Australia, and New Zealand. The survey comprised demographic information, educational background, and the influencing factors to academic career progression. Results: The vast majority of Chinese faculty members who worked in US were male. Individuals with junior academic title comprised the largest proportion. Over 75% of Chinese-origin pharmacy academics were involved in scientific disciplines (e.g., pharmaceutics, pharmacology, and medicinal chemistry). Usually, Chinese-origin academic members spent 4 years obtaining their first academic jobs after finishing PhD degree, and need 5–6 years to get academic promotion. The contributing factors of academic promotion were high quality publications and external funding. Conclusion: Our research offers a deep insight into academic career progression for URMs and give some valuable advice for their pharmacy academic paths.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".