Co-producing active lifestyles as whole-system-approach: theory, intervention and knowledge-to-action implications
Bibliographic record
Abstract
Population health interventions tend to lack links to the emerging discourse on interactive knowledge production and exchange. This situation may limit both a better understanding of mechanisms that impact health lifestyles and the development of strategies for population level change. This paper introduces an integrated approach based on structure-agency theory in the context of 'social practice'. It investigates the mechanisms of co-production of active lifestyles by population groups, professionals, policymakers and researchers. It combines a whole system approach with an interactive knowledge-to-action strategy for developing and implementing active lifestyle interventions. A system model is outlined to describe and explain how social practices of selected groups co-produce active lifestyles. Four intervention models for promoting the co-production of active lifestyles through an interactive-knowledge-to-action approach are discussed. Examples from case studies of the German research network Capital4Health are used to illustrate, how intervention models might be operationalized in a real-world intervention. Five subprojects develop, implement and evaluate interventions across the life-course. Although subprojects differ with regard to settings and population groups involved, they all focus on the four key components of the system model. The paper contributes new strategies to address the intervention research challenge of sustainable change of inactive lifestyles. The interactive approach presented allows consideration of the specificities of settings and scientific contexts for manifold purposes. Further research remains needed on what a co-produced knowledge-to-action agenda would look like and what impact it might have for whole system change.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".