Follow the Arrows: Using a Co-Created Causal Loop Diagram to Explore Leverage Points to Strengthen Population Physical Activity Promotion in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Population physical activity promotion (PPAP) is one of the most effective noncommunicable disease prevention strategies, yet coordination is lacking around the world. Whole-of-system approaches and complex systems methods are called for to advance PPAP. This paper reports on a project which (1) used an Attributes Framework with system mapping (group model building and causal loop diagramming of feedback loops) and (2) identified potential leverage points to address the challenge of effective coordination of multisectoral PPAP in British Columbia. METHODS: Key findings from stakeholder interviews and workshops described the current system for PPAP in terms of attributes and dimensions in the framework. These were translated into variables and used in group model building. Participants prioritized the importance of variables to address the coordination challenge and then created causal loop diagrams in 3 small groups. One collective causal loop diagram was created, and top priority variables and associated feedback loops were highlighted to explore potential leverage points. RESULTS: Leverage points included the relationships and feedback loops among priority variables: political leadership, visible policy support and governance, connectivity for knowledge translation, collaborative multisector grants, multisector collaboration, and integrating co-benefits. Leveraging and altering "vicious" cyclical patterns to increase coordinated multisector PPAP are key. CONCLUSIONS: The Attributes Framework, group model building and causal loop diagrams, and emergent feedback loops were useful to explore potential leverage points to address the challenge of multisectoral coordination of PPAP. Future research could apply the same methods in other jurisdictions and compare and contrast resultant frameworks, variables, feedback loops, and leverage points.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".