Beijing health promoting universities: practice and evaluation
Bibliographic record
Abstract
The aims of this study were to create a health promoting university within the framework of the Ottawa Charter for Health Promotion. Strategies included reforming and issuing healthy policies, creating a healthy physical and social environment, developing personal health skills, reorienting the health services, and implementing intervention activities. To evaluate the study, 180 students and 120 teaching/administrative staff were sampled for an in-depth interview with open-ended questions administered 1 year after the launch of the project. To assess health knowledge and behavior, 2500 students were sampled to answer a questionnaire, both prior to and following project implementation. With respect to policies, environment and health services, 166 students and 117 teaching and administrative staff participated in the in-depth interview. Approximately three-quarters (75.90%) of university students considered that the physical environment of the campus had improved significantly and 83.73% reported they had a good social environment. All university administration departments made commitments to health promotion. Consultations on mental health, smoking cessation and STD/AIDS prevention were provided all year round. Health education was included in a curriculum as a selective course with 1-2 credits. Almost two-thirds (60.66%) of teaching/administrative staff reported that they had had a yearly physical examination. In the final stages of the research, significantly more college students reported improved mental health (38.25% compared with 17.93% at baseline) (p < 0.01) and more were knowledgeable about transmission of STDs/AIDS (57.00/35.50% compared with 51.66/28.20% at baseline, respectively) (p < 0.01). Significantly less regular smokers were found (45% compared with 15.81% at baseline) (p < 0.01). However, there was a significant increase in high-fat food intake (44.81% compared with 49.50%) (p < 0.01) and pre-marital sex (5.11% compared with 14.00%), and a significant decrease in physical exercise participation (29.41% compared with 23.50%) (p < 0.01). As a health promotion setting, the university community can benefit greatly from implementing health promotion campaigns based on the principles of the Ottawa Charter.
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 | 0.059 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".