Mapping national capacity to engage in health promotion: Overview of issues and approaches
Bibliographic record
Abstract
This paper reviews approaches to the mapping of resources needed to engage in health promotion at the country level. There is not a single way, or a best way to make a capacity map, since it should speak to the needs of its users as they define their needs. Health promotion capacity mapping is therefore approached in various ways. At the national level, the objective is usually to learn the extent to which essential policies, institutions, programmes and practices are in place to guide recommendations about what remedial measures are desirable. In Europe, capacity mapping has been undertaken at the national level by the WHO for a decade. A complimentary capacity mapping approach, HP-Source.net, has been undertaken since 2000 by a consortium of European organizations including the EC, WHO, International Union for Health Promotion and Education, Health Development Agency (of England) and various European university research centres. The European approach emphasizes the need for multi-methods and the principle of triangulation. In North America, Canadian approaches have included large- and small-scale international collaborations to map capacity for sustainable development. US efforts include state-level mapping of capacity to prevent chronic diseases and reduce risk factor levels. In Australia, two decades of mapping national health promotion capacity began with systems needed by the health sector to design and deliver effective, efficient health promotion, and has now expanded to include community-level capacity and policy review. In Korea and Japan, capacity mapping is newly developing in collaboration with European efforts, illustrating the usefulness of international health promotion networks. Mapping capacity for health promotion is a practical and vital aspect of developing capacity for health promotion. The new context for health promotion contains both old and new challenges, but also new opportunities. A large scale, highly collaborative approach to capacity mapping is possible today due to developments in communication technology and the spread of international networks of health promoters. However, in capacity mapping, local variation will always be important, to fit variation in local contexts.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".