Evaluating the implementation of the WHO Healthy Cities Programme across Germany (1999-2002)
Bibliographic record
Abstract
The WHO Healthy Cities Project (1988) is a well-known example of the setting-based approach to health promotion. Developed as a framework for translating the key principles of the Ottawa Charter for Health Promotion (1986) into practice, it is best characterized as a process for successfully encouraging healthy public policy. In 2001, the German Healthy Cities Network (HCN) commissioned a survey of the 52 local Healthy Cities programme Coordinators (HCC) to monitor progress and identify strengths and weaknesses associated with its implementation. Most (90%; 47/52) HCC participated in the survey. Several positive aspects of the Health Cities Programmes (HCP) in Germany were identified: during the first 5 years, it expanded rapidly; project coordinators felt highly engaged, despite limited resources; a combination of traditional and innovative approaches was adopted and applauded; and almost 75% of HCC felt that their efforts had been beneficial. Nonetheless, the following shortcomings were identified: increased resources required; greater clarification of concepts and strategies at the local level; stronger commitment to the Nine-Point Programme of Action; greater integration within the national HCN and the local political administrative system (PAS); better programme documentation and evaluation. In conclusion, the HCN in Germany has expanded and developed since its inception 20 years ago. German HCP will only improve if professionalism and quality of local work are improved, particularly in terms of strengthening their influence on the local PAS and on public policies.
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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 | 0.057 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".