Revitalising the setting approach – Supersettings for sustainable action against lifestyle diseases
Bibliographic record
Abstract
The concept of health promotion rests on aspirations aiming at enabling people to increase control over and improve their health. The Ottawa Charter for Health Promotion specifies that health promotion action is facilitated in settings such as schools, homes and work places. In recognition of the ubiquitous and growing challenge of lifestyle diseases and of the importance of the social determinants and multisectoral nature of health, we reflect critically on the operational arm of health promotion and introduce a new concept, the supersetting approach, in an effort to harmonise the setting approach with contemporary realities (and complexities) of public health action. The supersetting approach is a whole-systems approach based on coordinated and integrated interventions in local communities. Interventions are implemented in multiple settings by multiple stakeholders all targeting a common goal such as lifestyle changes in a population. Interventions are not predetermined but developed and implemented jointly by partners and target-groups based on local needs, ideas and resources. The supersetting approach optimises the effectiveness of health promotion action by working through long-term intersectoral partnerships between civil society organisations, public institutions, private companies and academia. Participatory action research is a key strategy for empowering participants and generating knowledge about their perceptions of processes and actions. This knowledge is used to adjust interventions iteratively as the initiative unfolds. Realistic evaluation and “mixed methods” further optimises the evaluation and assessment of impact of complex interventions. Based on preliminary findings from an ongoing supersetting initiative addressing the prevention of lifestyle diseases in two Danish municipalities we discuss the benefits and challenges of establishing local community partnerships using the supersetting approach. Despite experiencing challenges related to diversity in personal and organisational motives for participation we argue that the supersetting approach is a promising framework for bringing diverse stakeholders together around constructive processes of joint planning and social action on health and development issues within the local community. Key messages The supersetting approach is a whole-systems approach based on coordinated and integrated interventions jointly developed and implemented by local partners in the local community. The supersetting approach is a promising framework for bringing diverse stakeholders together around constructive processes of joint planning and social action on health issues in the local community.
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 | 0.044 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.074 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.012 | 0.025 |
| Insufficient payload (model declined to judge) | 0.008 | 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".