3.O. Regular workshop: Health promotion in everyday settings. The Nordic way
Bibliographic record
Abstract
It is widely recognized that health promotion is a significant prerequisite to a sustainable society and an important complement to disease prevention. As suggested by the Ottawa charter effective health promotion should be conducted by utilizing a setting approach moving the target of the interventions from individuals or groups of individuals to their environments, the settings of everyday life (Kickbusch, 2003). A setting is therefore a meaningful way to look at and understand the living environments so that it can be made ready for implementation of health promotion action. The setting approach has been one of the most successful strategies in the implementation of health promotion and become a driving force in creating meaningful structures linking individuals and environments. A bigger challenge is linking the key settings into a coherent interactive entity. This workshop provides a system approach to the setting approach and discusses how the key stakeholders can implement an overall synergetic HiAP (Health in All Policies). One of the very underpinnings of health promotion ideology is user involvement. In this round table session the main objective is to show how health promotion thinking and action effectively can be applied in different settings. The session discusses the Nordic Model for public health and shows examples on health promotion research on settings like municipalities, school, work-life and health care. Key messages The Nordic way of thinking health promotion is gaining acceptance, how is this shown in policy? The settings approach is an important approach in Health Promotion Practice. we will show three examples
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.102 | 0.052 |
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".