Health Promoting Hospital: A Noble Concept
Bibliographic record
Abstract
Settings based approach to health promotion was founded in order to improve people’s health where they spend most of their time: in organizations, and it acknowledges that behavioural changes are only possible and stable if they are integrated into everyday life and correspond with concurrent habits and existing cultures. One of the key strategies identified in the Ottawa Charter was re-orientation of health services. Hospitals provide considerable opportunity to engage a broad section of the community through patients and their family members as well as their own staff and personnel. The WHO Regional Office for Europe started the first international consultations in 1988, followed by the European Pilot Hospital Project in 1993, in 20 partner hospitals from 11 European Countries. WHO Health Promoting Hospital movement focuses on four areas: promoting the health of patients, promoting the health of staff, changing the organization to a health promoting setting, and promoting the health of the community in the catchment area of the hospital. The International Network of Health Promoting Hospitals (HPH) acts as a network of networks linking all national/regional networks. In total, it consists of 38 National / Regional HPH Networks, collaborating to reorient health care towards active promotion of health, with 800 hospital and health service members in more than 40 countries. Health promoting hospital concept in India is very new and till date, only three hospitals are at various stages of being developed as HPH. However this initiative should evolve from the project mode to the programme mode.
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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.043 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".