Hotspots and Trends of Graduate Public Health Education Research in China
Bibliographic record
Abstract
Academic research trends involving public health education may reflect a certain degree of talent construction status. This study systematically reviews the data for published literature on graduate public health education, aiming to provide evidence for the optimization of public health postgraduate training mechanisms in China. Keyword cooccurrence analysis was performed based on high-frequency keywords. From 1992 to 2008, the annual number of publications in this field was relatively low in China, averaging fewer than 5 articles. The number of publications showed a steep increase after 2009. The publications were mostly from single research institutions, including comprehensive universities and military medical universities, concentrated in Beijing and Shanghai. The high-frequency keywords were public health and preventive medicine, postgraduate training, professional degree, MPH, curriculum, and teaching reform. Hotspots consisted of practical teaching research, training, educational reform and comparative education research. Research on public health postgraduate education has not reached scale and has insufficient support. Moreover, many problems in graduate public health education still cannot be solved by existing studies: authentic and practical learning, a unified approach to cultivate graduate students, organizational change of graduate public health education, and international cooperation and public health education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.029 | 0.045 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".