Healthy Universities for Healthy Communities: Bridging the Divide
Bibliographic record
Abstract
The Health Promoting Universities movement continues to mature.Universities have a unique opportunity to be societal leaders in promoting healthy settings for people to learn, work, play and love -recognizing that "health is created and lived by people within the settings of everyday life."(World Health Organisation, 1986) The movement is growing nationally and internationally with Higher Education networks -including the UK Healthy Universities Network, the Canadian Health Promoting Campuses Network, and most recently, the Scottish Healthy Universities Network (as part of the UK Healthy Universities Network).Together, this forms a firm basis for universities to collaborate on how to improve the health and wellbeing of students, staff, and local communities.In 2015, the University of British Columbia in Canada co-hosted the International Conference on Health Promoting Universities and Colleges, bringing together participants from 45 countries representing both educational institutions and health organizations, including the World Health Organization and UNESCO.Following months of pre-consultation and three days of vigorous discussion at the Conference, the Okanagan Charter: An International Charter for Health Promoting Universities and Colleges was born.The Charter states: "Health promoting universities and colleges transform the health and sustainability of our current and future societies, strengthen communities and contribute to the well-being of people, places and the planet."
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".