Standards for Health Promoting Hospitals and Health Services: Development and tools for implementation and measurement
Bibliographic record
Abstract
Abstract The need to reorient health services towards health promotion is greater than ever. Health systems are overburdened by treating an ever-growing number of chronic patients, many of which seek care for problems that could partly be avoided or postponed through better health promotion implementation. Since its establishment, the International Network of Health Promoting Hospitals and Health Services has explicitly addressed this issue by developing specific standards on evidence-based health promotion approaches and interventions that should be implemented in health services organizations. These approaches and interventions not only address the health of patients, but also of staff and the wider community. Since the development of the standards in 2006, health systems and legitimate patient demands have evolved considerably. At the same time, topics emerged that are strongly associated with health promotion strategies, such as the climate impact of health services. An update of the 2006 standards was therefore overdue. The purpose of this paper is, firstly, to describe the methodology used to develop and outline the 2020 Standards for Health Promoting Hospitals and Health Services. Secondly, we present a self-assessment tool, which was developed to operationalize and provide concrete measurable elements for each standard against which performance and progress towards implementation can be measured and tracked. The 2020 standards are health-oriented, continue to uphold the strategies defined in the Ottawa Charter for Health Promotion, and respond to recent international declarations and charters.
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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 | 0.252 | 0.289 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".